## 1. Introduction: How Should We Define Health and Disease?



One of the fundamental and most long-standing debates in the
philosophy of medicine relates to the basic concepts of health and
disease (see
 concepts of health and disease).
 It may seem obvious what we mean by such statements: people seek
treatment from medical professionals when they are feeling unwell, and
clinicians treat patients in order to help them restore or maintain
their health. But people seek advice and assistance from medical
professionals for other reasons, such as pregnancy which cannot be
construed as a disease state, and high blood pressure which is
asymptomatic. Thus the dividing line between disease and health is
notoriously vague, due in part to the wide range of variations present
in the human population and to debates over whether many concepts of
disease are socially constructed. One of the further complicating
factors is that both the concepts of health and disease typically
involve both descriptive and evaluatory aspects (Engelhardt 1975),
both in common usage among lay persons and members of the medical
profession.



Exploring these distinctions remains epistemologically and morally
important as these definitions influence when and where people seek
medical treatment, and whether society regards them as
"ill", including in some health systems whether they are
permitted to receive treatment. As Tristram Engelhardt has argued,




> 
> 
> the concept of disease acts not only to describe and explain, but also
> to enjoin to action. It indicates a state of affairs as undesirable
> and to be overcome. (1975: 127) 
> 
> 
> 



Hence how we define disease, health, and related concepts is not a
matter of mere philosophical or theoretical interest, but critical for
ethical reasons, particularly to make certain that medicine
contributes to people's well-being, and for social reasons, as
one's well-being is critically related to whether one can live a
good life.



The terms "disease" and "illness" often are
used interchangeably, particularly by the general public but also by
medical professionals. "Disease" is generally held to
refer to any condition that literally causes "dis-ease" or
"lack of ease" in an area of the body or the body as a
whole. Such a condition can be caused by internal dysfunctions such as
autoimmune diseases, by external factors such as infectious or
environmentally-induced diseases, or by a combination of these factors
as is the case with many so-called "genetic" diseases (on
the idea of genetic disease and associated problems, see for instance
Hesslow 1984, Ankeny 2002, Juengst 2004). It has been argued that
there is no philosophically or scientifically compelling distinction
between diseases and other types of complaints that many would not
consider to be diseases such as small stature, obesity, or migraine
headaches (Reznek 1987). The notion of "disease" is common
among most cultures, and may even be a universal concept (Fabrega
1979). It is a useful concept as it allows a clear focus on problems
that afflict particular human beings and suggests that medicine can
help to control or ameliorate such problems. In contrast,
"illness" is usually used to describe the more
non-objective features of a condition, such as subjective feelings of
pain and discomfort. It often refers to behavioral changes which are
judged as undesirable and unwanted within a particular culture, and
hence lead members of that culture to seek help, often from
professionals identified as health providers of some type within that
culture (on some of the complexities relating to the triad of concepts
"disease, illness, sickness", see Hofmann 2002).



The term "sickness" emphasizes the more social aspects of
ill health, and typically highlights the lack of value placed on a
particular condition by society. Disease conditions are investigated
not only to be understood scientifically, but in hopes of correcting,
preventing, or caring for the states that are disvalued, or that make
people sick. The classic work of the sociologist Talcott Parsons
(1951) showed how the "sick role" relieves one of certain
social responsibilities (for example, allows one to take time off work
or to avoid family responsibilities) and also relieves blame for being
ill (though not necessarily from having become ill in the first
place). Although there are exceptions and counterexamples to this
model (for example, some chronic diseases), it does fit our generally
accepted societal notions of what it means to be sick (and healthy),
and the moral duties and responsibilities that accompany the
designation of someone as sick.



The dominant approach in much of the recent philosophical scholarship
on the philosophy of medicine views disease concepts as involving
empirical judgments about human physiology (Boorse 1975, 1977, 1997;
Scadding 1990; Wachbroit 1994; Thagard 1999; Ereshefsky 2009). These
so-called "naturalists" (sometimes called
"objectivists", for example see Kitcher 1997, or
"descriptivists") focus on what is biologically natural
and normal functioning for all human beings (or more precisely human
beings who are members of relevant classes such as those within a
particular age group or of the same sex). They argue that medicine
should aim to discover and describe the underlying biological criteria
which allow us to define various diseases. Christopher Boorse's
revised account has been the most influential in the literature,
claiming that health is the absence of disease, where a disease is an
internal state which either impairs normal functional ability or else
a limitation on functional ability caused by the environment (Boorse
1997). "Normal functioning" is defined in terms of a
reference class which is a natural class of organisms of uniform
functional design (i.e., within a specific age group and sex), so that
when a process or a part (such as an organ) functions in a normal way,
it makes a contribution that is statistically typical to the survival
and reproduction of the individual whose body contains that process or
part. His definition includes specific reference to the environment so
as not to rule out environmentally-induced conditions which are so
common as to be statistically normal such as dental caries.



Many have criticized these approaches (to name just a few, Goosens
1980; Reznek 1987; Wakefield 1992; Amundson 2000; Cooper 2002), as
well as naturalistic accounts of disease more generally. As they have
noted, naturalistic accounts do not reflect our typical usage of the
terms "disease" and "health" because they
neglect to take into account any values which shape judgments about
whether or not someone is healthy. The usual counterexamples proposed
to naturalism are masturbation, which was widely believed to be a
serious disease entity in the 18th and 19th
centuries (Engelhardt 1974), and homosexuality, which for most of the
20th century was classified as a disease in the Diagnostic
and Statistical Manual (DSM) of the American Psychiatric Association.
These are counterexamples as their redefinitions as non-disease
conditions were due not to new biological information about these
states of being but changes in society's moral values.
Naturalists respond to such arguments by pointing out that
homosexuality and masturbation were never diseases in the first place
but erroneous classifications, and thus these examples do not affect
the validity of the definition of disease favored by them when it is
applied rigorously.



A more telling criticism of naturalism is that although its advocates
claim to rely exclusively on biological science to generate their
definitions of health and disease, these rely implicitly on an
equation of statistical and theoretical normality (or the
"natural state" of the organism), at least in
Boorse's formulation (Ereshefsky 2009). But biology does not
give us these norms directly, nor is there anything absolutely
standard in "species design" (as many philosophers of
biology have argued) despite Boorse's claims. No particular
genes are the "natural" ones for a given population, even
if we take a subgroup according to age or gender (Sober 1980). Nor
does standard physiology provide these norms (Ereshefsky 2009), not in
the least part because physiological accounts typically provide
idealized and simplified descriptions of organs and their functions,
but not of their natural states (Wachbroit 1994). Rachel Cooper (2002)
compellingly argues that coming up with an acceptable conception of
normal function (and in turn dysfunction) is the major problem with
Boorsian-style accounts, arguing that his analysis should focus on
disposition to malfunction instead. This argument utilizes
counterexamples such as activities that interfere with normal
functioning such as taking contraceptive pills that are not diseases,
as well as examples of persons with chronic diseases controlled by
drugs who function normally as a result. Elselijn Kingma (2007, 2010)
has critiqued Boorse's appeal to reference classes as
objectively discoverable, arguing that these cannot be established
without reference to normative judgments. A further issue often noted
with regard to naturalistic accounts of disease (for example, that of
Lennox 1995) is the underlying assumption that biological fitness
(survival and reproduction) is the goal of human life, and along with
this that medicine is only considered to be interested in biological
fitness, rather than other human goals and values, some of which might
indeed run contrary to or make no difference in terms of the goal of
biological fitness, such as relief of pain.



An alternative approach in the philosophical literature to
naturalist/descriptivist/objectivist definitions of disease and health
can roughly be termed "normative" or
"constructivist". Most proponents agree that we must
define the terms "disease" and "health"
explicitly and that our definitions are a function of our values
(Margolis 1976; Goosens 1980; Sedgewick 1982; Engelhardt 1986). Hence
defining various disease conditions is not merely a matter of
discovering patterns in nature, but requires a series of normative
value judgments and invention of appropriate terms to describe such
conditions. Conversely, health involves shared judgments about what we
value and what we want to be able to do; disease is a divergence from
these social norms. Normativists believe that their definitions are
valid not only philosophically but also reflect actual usage of the
terminology associated with disease and health both in common language
and among medical professionals. They also claim that this approach
more adequately explains how certain conditions can come to be viewed
in different ways over the course of history as our values changed
despite relatively few changes in our underlying biological theories
about the condition, for example homosexuality. Further, they are able
to accommodate examples of so-called folk illnesses or culture-bound
syndromes such as ghost sickness among some Native American tribes,
the evil eye in many Mediterranean cultures, or *susto* in
Latin and South American cultures, as their theories explicitly allow
for cross-cultural differences in understandings of disease and
health.



However normativism also generates a series of typical criticisms: it
cannot cope adequately with cases where there is general agreement
that a state is undesirable (such as alcoholism or morbid obesity) but
no similar general agreement that the state is actually a disease
condition (Ershefsky 2009). Another classic objection is that
normative accounts do not allow us to make retrospective judgments
about the validity of disease categories such as
"drapetomania" (a disease which was commonly diagnosed
among American slaves in the 19th century, with the main
symptom being the tendency to run away) (Cartwright 1851). The
normativist can point to changes in values to explain the abandonment
of belief in this disease condition, but would not be able to claim
that the doctors were in any sense "wrong" to consider
drapetomania to be a disease. Hence there is more involved in our
everyday usage of the terms "disease" and
"health" than just value or normative conditions.



Hybrid theories of health and disease attempt to overcome the gaps in
both the naturalistic and normative approaches, by hybridizing aspects
of both theories (Reznek 1987; Wakefield 1992; Caplan 1992). For
instance Jerome Wakefield (1992, 1996, 2007), writing about
psychiatric conditions in particular, notes that a condition should be
considered a disease if it both causes harm to the person or otherwise
contributes diminished value, and the condition results from some
internal mechanism failing to perform its natural function (hence for
instance much of what is diagnosed as "depression" would
fail to count as a disease condition). Whereas the normativist is
committed to calling any undesirable state a disease condition, these
hybrid criteria rule out calling conditions "diseases"
which are non-biological,. Then various marginal cases might be
considered to be healthy rather than potentially described as
diseased, and hence might not be eligible for treatment within
conventional medicine. Examples include those organs or structures
that no longer have a function due to evolutionary processes cannot
malfunction and so cannot be diseased. Many hybrid approaches also
retain too many assumptions about their naturalistic components, and
hence are criticized for relying on a notion of natural function which
cannot be supported by biology.



The concept of health has been relatively undertheorized in comparison
to those of disease and illness, perhaps in part because it raises
even more complicated issues than these concepts describing its
absence. One could be a straightforward naturalist about health, and
define it as being a product of a functional biology; however this
argument would run afoul of the same criticisms of naturalism
recounted above (see Hare 1986). The source for the classic definition
of health comes from the Constitution of the World Health Organization
(WHO) which defines health 



> 
> 
> a state of complete physical, mental and social well-being and not
> merely the absence of disease or infirmity. (WHO 1948: preamble) 
> 
> 
> 



Notice that according to this formulation, health is not just the
absence of disease but a positive state of well-being and flourishing
(notoriously ambiguous concepts in themselves). Although quality of
life is often cited as critical to definitions and theories of health,
many commentators are wary of the expansiveness of a definition
similar to the WHO's terminology, as it seems to encompass many
things beyond the health of the individual which could contribute (or
diminish) his or her "well-being".



A more narrow definition of health takes its rightful domain as being
the state which medicine aims to restore, and its opposite to be
"unhealth" or falling short of being healthy, rather than
disease as such (Kass 1975). Under such a definition, medicine should
not engage in aesthetic surgery or elective terminations of pregnancy
or similar procedures which do not (strictly speaking) seek to restore
health. Caroline Whitbeck (1981) has defined health in terms of the
psychological and physiological capacities of an individual that allow
him or her to pursue a wide range of goals and projects. Hence her
account is a type of hybrid approach, since she places biological
capacities at the core of her definition of health but only in so far
as they help individuals to flourish and live their lives as they wish
to do. The concept of health here is much more than the absence of
disease; for instance, one could have a high level of health while
still suffering from a particular disease condition.



One much discussed philosophical approach to defining health is that
of Georges Canguilhem (1991, based on work in the early 1940s), who
argued against equating it with normality. He noted that the concept
of a norm could not be defined objectively in a manner that could be
determined using scientific methods. Physiology deals with the science
of norms, but even scientifically-based medical approaches should not
focus solely on norms, contrary to for instance the ideal vision of
medicine according to Claude Bernard (1865). The history of how the
distinction between the normal and pathological became so entrenched
is explored in detail in Michel Foucault's now classic work
(1963). Both Foucault and Canguilhem sought to reveal how values have
been built into the epistemological framework underlying modern
medicine.



One of the key points in Canguilhem's argument is that our usage
of the term "normal" often conflates two distinct
meanings: the usual or typical, and that which is as it ought to be.
Consequently, he argues that there can be no purely scientific or
objective definition of the normal that allows us to take the theories
of physiology and apply them in medical practice, and accordingly we
cannot define health as normality either. Instead, according to him,
health is that which confers a survival value, particularly
adaptability within a set of environmental conditions: "to be in
good health is being able to fall sick and recover; it is a biological
luxury" (1991: 199).
Disease, then, is reduction in the levels of tolerance for the
vagaries of the environment. As Mary Tiles (1993) has noted, this
emphasis on health rather than normality is a particularly useful tool
for enriching contemporary debates over preventative medicine and more
generally the trend toward the development of a positive conception of
health. Havi Carel (2007, 2008) has contributed to this strand of
thought, developing a phenomenological notion of health which
emphasizes that health should be understood as the lived experience of
one's own body rather than as simply statistically normal bodily
functioning in abstract biological terms. Hence she develops an
expressly revisionist project, emphasizing that a phenomenological
perspective accommodates cases where someone is ill (in biological
terms) but healthy, such as in chronic illness.



A number of authors have made even more extreme claims, arguing that
seeking concepts of disease is bound to be a failed effort. For
instance, Peter Schwartz (2007) claims that there is not an underlying
general concept of disease within the biomedical sciences that is
coherent enough to be analyzed, and that different concepts of disease
might be useful within different contexts. Some philosophers have
argued that to seek correct definitions for "disease" and
"health" is distracting and irrelevant when it comes to
clinical decisions: as Germund Hesslow puts it, "the
health/disease distinction is irrelevant for most decisions and
represents a conceptual straightjacket [sic]" (1993: 1). The key is whether or not a
particular state is desirable to its bearer, and not whether the
person actually has a disease or defect. For instance, the term
"malady" has been proposed as a more appropriate
alternative to "disease" (Clouser, Culver, and Gert 1981),
and which should be extended to include all illnesses, injuries,
handicaps, dysfunctions, and even asymptomatic conditions. A malady is
present when there is something wrong with a person; regardless of the
cause (mental or physical), to be a malady, the condition must be part
of its bearer and not distinct or external to him or her. The clear
advantage of this approach is that it unifies a range of phenomena and
descriptions that seem intuitively to be related. The disadvantages
include that it relies in part on an objectivist approach to disease,
and hence suffers from some of the difficulties detailed above that
plague some versions of naturalism (for a provocative reaction to this
debate, see Worrall and Worrall 2001).



An alternative approach to defining disease and health has been
described by Marc Ereshefsky (2009) in terms of making distinct state
descriptions (descriptions of physiological or psychological states
while avoiding any claims about naturalness, functionality, or
normality), and normative claims (explicit judgments about whether we
value or disvalue a particular physiological or psychological state).
This approach has the advantages of allowing more clarity about
controversial "disease" conditions as it avoids the need
to apply the term explicitly. It also forces us to pinpoint the key
issues that matter to understanding and treating someone suffering
from ill health. But perhaps most persuasively, he argues that this
approach allows us to distinguish the current state of a human from
those we wish to promote or diminish, whereas the terms
"disease" and "health" do not adequately
highlight this critical distinction.



In short, philosophers of medicine continue to debate a range of
accounts: in broad outline, the most vigorous disagreement centers on
whether more objective, biologically-based, and generalizable accounts
are preferable to those that incorporate social and experiential
perspectives. It is clear that none satisfy all of the desiderata of a
complete and robust philosophical account that also can be useful for
practitioners; although some would dispute whether the latter should
be a requirement, many believe that philosophy of medicine should be
responsive to and helpful for actual clinical practices.


## 2. Contested and Controversial Disease Categories



Some disease categories are far from straightforward in terms of being
recognized, named, classified, and made legitimate both within
medicine itself and for the wider society. In recent times there have
been long-standing debates over a range of conditions including Lyme
disease, fibromyalgia, and chronic fatigue syndrome (CFS), to name
just a few (for extended historical discussions of these and related
conditions, see Aronowitz 1998, 2001; Shorter 2008). Take CFS as an
example: its main symptoms are fatigue after exertion over a period
lasting at least six months, but sufferers can have a wide array of
complaints in diverse systems of the body; the range of severity is as
wide as the range of symptoms. The condition has been associated with
several other controversial syndromes and sometimes equated to with
them, most notably myalgic encephalitis and fibromyalgia, as well as
other illnesses of inexact definition such as multiple chemical
sensitivity and irritable bowel syndrome; more popular (and
derogatory) labels also have been attached to it such as yuppie flu.
Definitive evidence as to the cause or basis of CFS has remained
elusive, and in the absence of causal explanations, accurate diagnoses
and effective treatments often have been difficult to obtain. Thus the
illness has been perceived by many as being illegitimate because of
difficulties in proving the existence of a discrete disease condition,
given the lack of traditional forms of clinical evidence for it, and
it has had different statuses in different locales (see Ankeny and
Mackenzie 2016). These issues severely impact on the lives of those
affected by this condition, and on the care that is thought to be
appropriate to be made available to them.



Mental illnesses (and the term "mental health" itself)
also have traditionally posed considerable problems for categorization
and conceptualization for both medical practitioners and philosophers
of medicine. Many authors advocate the case that it is critical to
make a distinction between mental and physical illness (Macklin 1972),
particularly because of the moral implications associated with
labeling a condition as mental or psychological. Psychiatry is a field
which has historically been loaded with value judgments, many of which
were quite dubious. There is a long history of using mental illness as
a way to categorize behaviors which are socially deviant as well as
those conditions of ill health with no apparent organic cause and
which do not otherwise fit into our dominant biomedical model. Many
scholars (e.g., Ritchie 1989; Gaines 1992; Mezzich et al. 1996;
Horwitz and Wakefield 2007; Demazeux and Singy 2015) have critiqued
approaches and the underlying assumptions of the various editions of
the Diagnostic and Statistical Manual published by the American
Psychiatric Association, which is a "bible" for
psychiatric conditions for many practitioners and also has
considerable public influence for instance on who can seek care. Key
examples of contested issues within the DSM include the highly
politicized nature of the processes of revision across various
editions, various cultural, sexist, and gender biases inherent in
specific diagnostic categories, and the relatively weak reliability
and validity of the classification system.



One key question is whether the biomedical model is the most
appropriate approach to psychological or mental conditions and their
treatment. Some theorists have argued in favor of naturalistic
accounts of disease, notably Thomas Szasz (1961, 1973, 1987). As a
result, he famously claimed that "mental diseases" are a
myth and do not exist because they do not result from tissue damage;
in his view, all diseases must be correlated with this sort of
physical damage. He thus argues that the concept of mental illness is
a prescriptive concept used as though it were a merely descriptive
one, and also a justificatory concept masquerading as an explanatory
one. These conclusions lead him to a highly critical analysis of
psychiatric practices, and to reclassifying such forms of suffering as
"problems in living" rather than diseases. However it is
not always clear in his account what his evidence for these claims is,
and in particular whether he is making an in principle objection or
one that is grounded in the history of the mistreatment of people with
mental illnesses, and the disservice done to them in part because of
the adoption of the medical model. In addition, some have noted that
some psychiatric conditions do in fact correlate with physiologically
detectable and other types of biological abnormalities. For instance
twin studies have demonstrated that genetics is a major factor in the
etiology of schizophrenia among other conditions typically considered
to be psychiatric, although clearly not all conditions that are
diagnosable according to contemporary psychiatric standards fit this
model.



A prominent functionalist approach to mental disorders more recently
has been that of Wakefield (1992, 1996, 2007), as discussed above, who
argues that mental disorders are best understood as
"harmful" dysfunctions, which permits a supposedly
value-free foundation in terms of biological function gauged in
evolutionary terms) with judgments coming in only in terms of the
judgment of whether certain dysfunctions are harmful to their bearers.
Such accounts have been criticized along lines similar to analyses of
Boorsian accounts by emphasizing that function and dysfunction cannot
in fact be defined independently of value terms, but Wakefield's
account also has been questioned in terms of its practical
implications (e.g., Sadler and Agich 1995) and whether malfunction is
a necessary component of mental disorder (Murphy and Woolfolk
2000).



Other authors, notably George Engel (1977), have argued for the need
to unify our understandings of mental and physical illness under a
broader, biopsychosocial model. Such a model would focus clinicians to
take account of both the physical, psychological, and social factors
that contribute to ill health, in contrast to the traditional
biomedical model which is faulted for being overly reductionistic
rather than holistic. Such an account, it is claimed, would be more
effective in dealing with borderline cases including people who are
told they are in need of treatment due to abnormal lab results or
similar but who are feeling well, as well as those who appear to have
no underlying somatic disease condition but are feeling unwell. Hence
this type of account does not draw any sharp distinction between the
physical and the mental (or even the social), leaving the question of
appropriate therapies or approaches as a matter to be decided by the
doctor and his or her patient. Engel compellingly defended this type
of account as more appropriate not only for clinical work but for
research and teaching in medicine. It is arguable that it has
implicitly (and often explicitly) been adopted in much of current-day
medical practice and teaching, although it is less clear whether it
has had much influence in biomedical research, much of which tends to
remain more reductionistic in its nature.


## 3. Theories, Causes, and Explanations in Medicine



There is no widely accepted notion of what a scientific theory is. The
logical positivists thought that theories are sets of
propositions, formalizable in first-order logic, at one point, and as
classes of set-theoretic models at another. For our purposes here one
can distinguish two senses of theory, a narrower and a broader sense.
In the narrower sense, a theory comprises a set of symbols and
concepts used to represent the entities in a domain of discourse as
well as a set of simple general-purpose principles that describe the
behavior of these entities in abstract terms. In the broader sense,
theory refers to any statement or set of statements used to explain
the phenomena of a given domain.



In medicine one can find theories in both the narrower and the broader
sense. Humorism, for instance, holds that the human body is filled
with four basic substances or "humors": black bile, yellow
bile, phlegm, and blood. The humors are in balance in a healthy
person; diseases are explained by excesses or deficiencies in one or
more humors. Humorism has ancient origins and influenced Western
medicine well into the 18th century. Eastern medicine has analogous
systems of thought. Indian Ayurveda medicine, for example, is a theory
of the three primary humors wind, bile, and phlegm, and diseases are
similarly understood as imbalances in humors (Magner 2002).



In contemporary Western medicine, such highly unifying and general
theories play a limited role, however. Evolutionary and Darwinian medicine may
well constitute exceptions but these are at best emergent fields at
present (see Methot 2011). Contemporary Western medical
researchers and practitioners instead seek to explain medical outcomes
using mechanistic hypotheses about their causes--symptoms by
hypotheses about diseases, diseases by hypotheses about antecedents,
epidemics by hypotheses about changes in environmental or behavioral
conditions (Thagard 2006). What distinguishes these contemporary
medical theories from the ancient approaches is that the causes of
symptoms, diseases, and epidemics can in principle be as multifarious
as the outcomes themselves; in the ancient approaches, lack of humoral
balance was the only possible cause. In contemporary Western medicine, 
there is no presupposition concerning number, form, or mode of action 
of the causes that explain the outcome other than there *being* 
some cause or set of causes responsible.



Not every cause is equally explanatory. A given person's death
can be described as one by cardiac arrest, pulmonary embolism or lung
cancer, for instance. The lung cancer may have had a genetic mutation,
the deposition of carcinogens in lung tissue and smoking in its causal
history. The smoking, in turn, was caused by the smoker's
proneness to addictive behavior, peer pressure and socio-economic
environment, let us suppose. Which of the many candidate hypotheses of
the form "*X* causes (or caused) *Y*", where
*Y* refers to the patient's death, does best explain the
outcome? There is no absolute answer to this question. The goodness of
a medical explanation depends in part on the context in which it is
given (see entry on
 scientific explanation).
 When asked "Why did *Y* happen?" a coroner might
refer to the pulmonary embolism, the patient's physician to the
lung cancer and an epidemiologist to the patient's tobacco
consumption. The adequacy of a medical explanation is related to our
ability to intervene on the factor in question. A pulmonary embolism
can be prevented by screening the patient for blood clots. The
accumulation of carcinogens in lung tissue can be prevented by
stopping smoking. By contrast, even though certain kinds of genetic
mutations are in the causal history of any cancer, the mutation is not
at present of much explanatory interest to most clinicians, as this is
not a factor on which they can easily intervene. There is considerable
current medical research to identify mutations associated with various
subtypes of cancer and using these to develop targeted therapies and
interventions, as well as to provide more accurate prognostic
information. Medical explanation, thus, is closely related to our instrumental 
interests in controlling, preventing and controlling outcomes (Whitbeck 1977).



One issue that is currently debated in the philosophy of medicine is
the desirability (or lack thereof) of citing information about the
mechanisms responsible for a medical outcome to explain this outcome.
While mechanisms are usually characterized in causal terms (e.g.,
Glennan 2002; Woodward 2002; Steel 2008), it is not the case that
every cause acts through or is a part of some mechanism, which is
understood as a more or less complex arrangement of causal factors
that are productive of change (e.g., Machamer et al. 2000). Absences,
such as lack of sunlight, can cause medical outcomes but are not
related to them through continuous mechanisms from cause to effect
(Reiss 2012). Neuroscientific explanations are often acceptable
despite the lack of knowledge or false assumptions about mechanisms
(Weber 2008). However, we may ask whether mechanistic explanations are 
generally *preferable* to non-mechanistic causal explanations.



Many medical researchers and philosophers of medicine subscribe to a
reductionist paradigm, according to which bottom-up explanations that
focus on the generative physiological mechanisms for medical outcomes
are the only acceptable ones or at least always preferable. Indeed,
macro-level claims such as "Smoking causes lung cancer"
seem to raise more questions than they answer: Why does smoking have
adverse health consequences? To prevent these consequences, is it
necessary to stop smoking? Is it possible to produce cigarettes the
smoking of which has fewer or no adverse consequences? What is the
best policy to improve morbidity and mortality from lung cancer?
Knowing that it is specific carcinogens in tobacco smoke and genetic
susceptibility that are jointly responsible for the onset of the
disease helps to address many of these questions.



Nevertheless it would be wrong to assume that we cannot explain
outcomes without full knowledge of the mechanisms responsible. When,
in the mid-1950s, smoking was established as a cause of lung cancer,
it was certainly possible to explain lung cancer epidemics in many
countries where people had exchanged pipe smoking for cigarette
smoking half a century earlier--even though the mechanism of
action was not understood at the time. Differences in lung cancer
incidence between men and women or between different countries can be
explained with reference to different smoking behaviors. Policy
interventions, in this case the addition of warning labels to
cigarette packets, could not wait until sufficient mechanistic
knowledge was available, nor did they have to wait.



For reasons such as these, a number of philosophers of medicine have
proposed to adopt an "explanatory pluralism" for medicine
(De Vreese et al. 2010; Campaner 2012). If nothing else, this is
certainly a position that is consistent with the explanatory practices
in the field.


## 4. Reductionism and Holism in Medicine



As in many fields, debates over reductionism versus holism are rife in
medicine both with reference to medical research and practice, and the
terms often are used rather loosely to mean a range of things (for a
related discussion see entry on
  reductionism in biology).
 In the broadest terms, reductionistic approaches to disease look for
fundamental mechanisms or processes that are the underlying causes of
that disease. In recent years in light of large-scale genomic
sequencing initiatives notably the Human Genome Project, there has
been considerable emphasis on reducing diseases to the genetic or
molecular level. Those who advocate more holistic approaches note that
reductionism leaves out important information based on the
patient's experiences of the disease at the phenotypic level,
and such information is critical to pursuit of effective treatments.
Many diseases typically viewed as "genetic" have proven to
be extremely difficult in practice to reduce to unified disease
entities with singular (or simple) genetic causes, including mental
illnesses (Harris and Schaffner 1992), cystic fibrosis (Ankeny 2002),
and Alzheimer's disease (Dekkers and Rikkert 2008). As Catherine
Dekeuwer (2015) notes, given that there probably is genetic variation
in susceptibility to virtually all diseases, there is no clear
demarcation between genetic diseases and diseases for which there are
genetic risk factors; hence she argues that our tendency to focus on
genetic determinants of disease may reinforce folk notions of the
geneticization of both people and of human behavior.



With regard to research, critics of reductionism point out that there
has been an overemphasis on the pursuit of genetic or molecular level
explanations of disease to the neglect of alternative levels of
explanation. Further, such limitations are highly detrimental to
patients, especially because there are not likely to be short-term
cures or treatments for most genetic diseases, perhaps beyond avoiding
having children carrying particular genes in the first instance (see for instance 
Hubbard and Wald 1999), although this domain of medicine is
rapidly changing as new treatments are developed and the
understandings of the effects of genomic mutations improve. Focusing
overly or solely on the genetic level results in a process which the
sociologist Abby Lippmann (1991) terms "geneticization",
namely reducing the differences between individuals to their DNA, and
in turn viewing genetics as the most promising approach to curing
disease, rather than viewing people and the illnesses that they suffer
at a phenotypic and much more environmentally-situated level. In
addition, as Elisabeth Lloyd (2002) argues, higher levels of social
organization that are culturally sanctioned have unrecognized causal
effects on health, and hence medical research should not be restricted
solely to the molecular level.



Fred Gifford (1990) claims that although all phenotypic traits are the result of an
interaction between genes and the environment within which they are
expressed, nonetheless it makes sense to distinguish certain traits as
"genetic"; he argues in terms of populations that if it is
genetic differences that make the differences in that trait variable
in a given population, and if genetic traits can be individuated in a
way that matches what some genetic factors cause specifically, then a
trait (including a disease trait) can be understood as genetic. Kelly
Smith (1992) disputes this, noting that the second condition depends
on an extremely problematic distinction between causes (in this case
genes) and mere conditions (e.g., epigenetic factors). Lisa Gannett
(1999) argues for a "pragmatic" account of genetic
explanation, claiming that when a disease is classed as
"genetic", the reasons for singling out genes as causes
over other conditions necessarily include pragmatic dimensions
inasmuch as they are relative to a given causal background (which
includes both genetic and nongenetic factors), relative to a
population, and relative to our present state of knowledge. More
recently it has been argued that although explanatory reduction cannot
be defended on metaphysical grounds, reductive explanations might be
indispensable ways to address certain questions in the most accurate,
adequate, and efficient ways (van Bouwel et al. 2011).


## 5. Randomized Controlled Trials and Evidence-Based Medicine



"Evidence-based medicine" (EBM) describes a movement that
was started (under that name) in the early 1990s by a group of
epidemiologists at McMaster University in Hamilton, Canada, as a
reaction against what was perceived as an over-reliance on clinical
judgment and experience in making treatment decisions for patients.
According to a widely cited definition: 



> 
> 
> Evidence based medicine is the conscientious, explicit, and judicious
> use of current best evidence in making decisions about the care of
> individual patients. (Sackett et al. 1996: 312) 
> 
> 
> 



Such a definition has bite only when the concept of evidence used is
relatively narrow. In particular, it should not allow clinical
judgment and experience to count as "best evidence".



To this effect, proponents of EBM have developed so-called
"hierarchies of evidence" that categorize different
research methods with respect to their supposed quality. While there
is no universally accepted hierarchy, the different proposed
hierarchies all agree in the priority they give to randomized
controlled trials (RCTs) and reviews thereof. A typical hierarchy
looks as follows (Weightman et al. 2005):




| Level of evidence | Type of evidence |
| --- | --- |
| 1++ | 
High quality meta-analyses, systematic reviews of RCTs (including
cluster RCTs), or RCTs with a very low risk of bias |
| 1+ | 
Well conducted meta-analyses, systematic reviews of RCTs, or RCTs with
a low risk of bias |
| 1-\* | 
Meta-analyses, systematic reviews of RCTs, or RCTs with a high risk of
bias |
| 2++ | 
High quality systematic reviews of, or individual high quality
non-randomised intervention studies (controlled non-randomised trial,
controlled before-and-after, interrupted time series), comparative
cohort and correlation studies with a very low risk of confounding,
bias or chance |
| 2+ | 
Well conducted, non-randomised intervention studies (controlled
non-randomised trial, controlled before-and-after, interrupted time
series), comparative cohort and correlation studies with a low risk of
confounding, bias or chance |
| 2-\* | 
Non-randomised intervention studies (controlled non-randomised trial,
controlled before-and-after, interrupted time series), comparative
cohort and correlation studies with a low risk of confounding, bias or
chance |
| 3 | 
Non-Analytical studies (e.g., case reports, case series) |
| 4 | 
Expert opinion, formal consensus |



Evidence produced by RCTs has thus been called the "gold
standard" of evidence in EBM (e.g., by Timmermans and Berg
2003).



In an RCT, a population of individuals who might benefit from a new
medical treatment are divided into a treatment group--the group
whose members receive the new treatment--and one or several
control groups--groups whose members receive either an
alternative or "standard" treatment or a
placebo. Individual patients are assigned to a group by means of a random process such as the flip of a coin. A placebo is an intervention that resembles the new treatment in
all respects except that it has no known ingredients active for the
condition under investigation (i.e., it is some kind of "sugar
pill"). Patients, researchers, nurses, and analysts are all
blinded with respect to treatment status of all patients until after
the analysis. After a period of time, a pre-determined outcome
variable is observed and the values of the variable are compared
between the groups. If the value of the outcome variable differs
between different treatment groups at the desired level of statistical
significance, the treatment is judged to be effective.



Proponents of EBM regard RCTs as reliable means to judge treatment
efficacy because they can help to control for a variety of (though not
all) biases and confounders. If, for instance, the symptoms of a
patient or group of patients improve after an intervention, this may
be due to spontaneous remission rather than the treatment. An
experimental design that compares a treatment group with one or
several control groups is therefore better able to control for this
confounder than a simple "before-and-after" design.
Similarly, a design in which the allocation to treatment and control
groups is done by a non-random process, it is possible that healthier
patients end up in the treatment and less healthy patients in the
control group. If so, the measured improvement may be due to the
health status of the patients rather than the intervention. Especially
if the allocation is done by a medical researcher who has a stake in
the matter (for instance because she has developed the new treatment),
allocation decisions may consciously or subconsciously be influenced
by expectations about who will profit from the intervention and thus
create unbalanced groups. Allocation by a random process helps to
control this source of bias.



No one denies that RCTs are powerful experimental designs--and
that their power stems from the ability to control numerous sources of
bias and confounding. However, to refer to RCTs as the "gold
standard" of evidence suggests that they are more. Specifically,
one may be led to assume that RCTs are necessary for reliable causal
inference or that RCTs are guaranteed to deliver reliable results. A
number of philosophers of medicine have in the past decade or so
argued that these stronger claims do not hold to scrutiny.



In particular, the following claims have been criticized:


* *The logic of statistical significance tests requires
randomization* (Fisher 1935). Ronald Fisher invoked his famous tea
lady thought experiment in order to make plausible that significance
testing works only with randomized allocation. Suppose an English lady
claims that she is able to tell whether tea or milk was poured into
the cup first and we would like to test this assertion. If she gets it
right each time in a series of eight cups (four "milk
first" and four "tea first" cups), this result may
be due to her usually sharp sense of taste. But it may also be for
indefinitely many other reasons: she may know that milk was poured
first in the first four cups and correctly identified the first four
as "milk first" cups; the "milk first" cups
differ in color or shape from the "tea first" cups or have
any other visually identifiable features; a confederate recorded which
cups were "milk first" and signals her; and so on. Fisher
now argues that only if the allocation of tea to cups was done at
random, the probability of the lady getting all eight cups right is
correctly identified as the probability of her getting it right if she
were to guess, having no discriminatory ability (which in this case is
1/70). Therefore, we can judge that she really does have an unusual
discriminatory ability or something very unlikely must have happened
(i.e., an event with the probability 1/70). But this is incorrect. In
fact, there is still an indefinite number of ways in which she got the
result even though she does not have a good sense of taste. If a
confederate signals her the correct answer, the probability of her
getting it right is very close to 1 independently of her
discriminatory ability (Worrall 2007a). A good experiment would
prevent this, but this has to do with other aspects of the
experimental design, not randomization.
* *Randomization controls for all confounders, known and
unknown* (Fisher 1935; Giere 1984). Many variables affect a
patient's probability of recovery: her gender, age,
co-morbidities, genetic factors, compliance with the treatment regime,
psychological factors and many more. If we want to judge that an
observed difference in recovery rates between treatment groups is due
to the intervention rather than these other factors, we have to make
sure that the probability distribution of causal factors is the same
between the different groups. Randomization is supposed to ensure
this. However, for any finite test population size (and many RCTs do
indeed have relatively small numbers of patients), it remains possible
that treatment groups are unbalanced: old patients ending up in one
group, younger in the other etc. While it is the case that the larger
the number of patients in the RCT, the less likely it is that the
groups are unbalanced with respect to any given factor, if there are
many possible factors affecting the outcome it is actually very likely
that some of them are unbalanced. Thus, in practice if it is noticed
after randomization that the two groups are unbalanced with respect to
a variable that is thought to affect the outcome outcome, then the
groups are re-randomized or adjusted (Worrall 2002)
* *It is possible to "prove" the results of an RCT
to be correct* (Cartwright 1989; cf. Worrall 2007b). Every
scientist, at some point in his career, learns that one cannot judge
*X* to be a cause of *Y* just because *X* and
*Y* are correlated. According to a prominent theory of
causation, viz. the probabilistic theory, causation is a form of
correlation after all. Very roughly, the probabilistic theory holds
that *X* causes *Y* just in case *X* and
*Y* are correlated and all sources of confounding have been
controlled (Reiss 2007). It can now be shown that under the
probabilistic theory and a host of other assumptions (including the
assumption that randomization has been successful in that the
treatment groups are balanced with respect to prognostic factors), if
the treatment status variable is correlated with the outcome variable,
then the treatment must cause the outcome (Cartwright 2007). To give
RCTs a special status in EBM on the basis of this reasoning would be
to commit a logical mistake, however. The argument can only show that
*if all the assumptions behind an RCT are satisfied*, the RCT
will give a causally correct result. It does not show that RCTs are
the *only* way to generate provably correct results. Indeed, it
can relatively easily be shown that observational studies that
identify so-called instrumental variables are similarly provably
correct under a certain set of assumptions (Reiss 2005).



A final but very important issue is that of the external validity of
the RCT results. Even under ideal conditions (i.e., when medical
researchers have very strong reasons to presume the assumptions under
which an RCT works to be satisfied), the RCT can only establish that
the treatment is effective *in the test population*. Typical
test populations differ from the target populations (i.e., those
populations for whom the treatment has been developed and who will
eventually receive the treatment) in more or less systematic ways. For
example, many RCTs will exclude elderly patients or patients with
co-morbidities but the treatment will be marketed to these patients.
For financial reasons, many RCTs are nowadays conducted in developing
countries whereas the treatments are mainly or exclusively marketed to
patients in developed countries. Whereas the protocols for conducting
an RCT are very strict and detailed, there are no good guidelines how
to make treatment decisions when the patient at hand belongs to a
population that differs from the population in which the RCT was
conducted (e.g., Cartwright 2011).



There are in fact two problems of external validity in the application
of RCT results. On the one hand there is the population-level problem
of making an inference from *test* to *target
population*. On the other hand, there is the problem of making an
inference from *population* to *individual*. The RCT
provides evidence for a population-level claim: "In population
*p* (the test population) intervention *X* is effective
in the treatment of condition *Y*". For this claim to be
true, the treatment must be on average effective, which allows the
effectiveness to vary among the individuals in the population. Indeed,
it is possible that the intervention is effective (and beneficial) on
average but ineffective or positively harmful in some individuals
(i.e., members of some subpopulations). Proponents of EBM to some
extent oversell their case when they write that EBM 



> 
> 
> de-emphasizes intuition, unsystematic clinical experience, and
> pathophysiologic rationale... and [instead] stresses the
> examination of evidence from clinical research. (Evidence-Based Medicine Working Group 1992)
> 
> 
> 
> 



because inferences from test to target population and from any
population to the individual receiving the treatment are necessarily
based on clinical judgment.



John Worrall argues that, at the end of the day, RCTs are a powerful
means to control selection bias, but no more than that (Worrall 2002,
2007a,b). As he uses the term, selection bias occurs when treatment
and control group are unbalanced with respect to some prognostic
factors because a medical researcher has selected which patients will
receive which treatment. Selection bias in this sense obviously cannot
occur in an RCT because in an RCT the allocation is made by a random
process. But it is also clear that randomization is at best sufficient
but not necessary to achieve the result. A large number of alternative
designs may be used to the same effect: allocation can be made by a
strict, albeit non-random protocol; allocation is made by non-experts
who are unrelated to the treatment development and therefore have no
expectations concerning outcomes; treatment and control groups are
deliberately matched (again by persons who have nothing at stake or
according to some protocol); and so on.



A controversial issue is the role of mechanistic knowledge, that is,
knowledge about the biological and physiological mechanisms
responsible for medical outcomes (and thus treatment efficacy) should
play in EBM. As mentioned above, the RCT provides evidence for
black-box causal claims of the form "In population *p*,
intervention *X* is effective in the treatment of condition
*Y*". As we have seen, proponents of EBM also believe EBM
to de-emphasize patho-physiologic rationale (a different term for
"mechanistic knowledge"). Nevertheless, a number of
philosophers of medicine have pointed out that mechanistic knowledge
is in fact important in EBM or that it should receive more attention.
Federica Russo and Jon Williamson have, for instance, argued that
causal claims need both statistical evidence as well as evidence about
the mechanisms that connect an intervention with the outcome variable
in order to be established (Russo and Williamson 2007). Others
disagree (Reiss 2012) or qualify the claim (Gillies 2011; Howick
2011a; Illari 2011). Further, it has been pointed out that mechanistic
knowledge plays an important role in the design and preparation of an
RCT, as well as in the interpretation and application of RCT results
(La Caze 2011; Solomon 2015). Especially when it comes to
extrapolating research results from a test to another population,
mechanistic knowledge is supposed to be vital (Steel 2008; see also
next section). On the other hand, knowledge about mechanisms is often
highly problematic and should not be relied on too heavily in
applications (Andersen 2012).


## 6. Animal Models



New therapies are often trialed using animal models before they are
tested on humans in a randomized trial. Animal models also play
important roles in establishing whether or not a substance is toxic
for humans. The International Agency for Research on Cancer (IARC),
for example, classifies substances with respect to the quality of the
evidence for their carcinogenicity into five groups. Evidence from
animal models is referred to in the characterization of each group
(IARC 2006). This raises questions about how such extrapolations from
animal models to humans work, and how reliable they are.



Animal models are widely used in biomedical research because
experimental interventions on animals are easier to conduct and
cheaper than experiments on humans. Both kinds of experiments involve
ethical dilemmas, but animal experimentation is usually regarded as
less problematic from an ethical point of view than experimentation
with humans. At any rate, the number of animals killed, maimed, or
made sick in biomedical research is much higher than the number of
humans adversely affected in this research.



There is a fundamental inferential problem in transferring what has
been learned in any model (whether human, animal, or whatever) to some
target population of interest has been described as the
"experimenter's circle" (Steel 2008). The problem is
essentially this. What is true of a model can be presumed to be true
of the target only to the extent that the model is similar to the
target in relevant respects. The reason we experiment on models in the
first place is, however, that the model differs in important respects
from the target (if animals were just like humans, we would not find
experiments on the former to be ethically less problematic than
experiments on the latter). Extrapolation--the inference from
model to target--is therefore only worthwhile to the extent that
there are significant limitations in our ability to study the target
directly. If so, there can be no good grounds to decide whether a
model is a good one for the target. To do so, we would have to
investigate whether the target is relevantly similar to the model; but
if we could do so, there would be no reason to study the model in the
first place.



This inferential problem has led some commentators to maintain highly
skeptical views concerning our ability to use animals as models for
humans in biomedical research. Hugh LaFollette and Niall Shanks argue
that animal models cannot be reliably used for extrapolation at all,
but at best only heuristically, as sources of hypotheses that have to
be tested on humans (LaFollette and Shanks 1997). They introduce two
terms to make their argument: causal analogue model (CAM) and
hypothetical analogue model (HAM). The former can be used to make
reliable predictions about target populations of interest; the latter
only heuristically. The main premise in their argument that animal
models in biomedical research are at best HAMs but not CAMs is that
for a model to be a CAM there cannot be causally relevant disanalogies
between model and target--a condition which is rarely if ever met
by animal models (again, this is why we study animals in the
laboratory in the first place).



Daniel Steel (2008: ch. 5) argues that LaFollette and Shanks'
condition for reliable extrapolation is too stringent. Whether a claim
about a model can be extrapolated depends, he argues, also on the
strength of the claim to be exported. It is one thing, say, to reason
from 



*x*% of the members of population *p* will show symptoms
of poisoning after ingesting substance *S* 



to 



*x*% of the members of population \(q \ne p\) will show
symptoms of poisoning after ingesting substance *S*, 



quite another to reason from the quantitative claim to a qualitative
claim such as "Substance *S* is poisonous for the members
of *q*".



Steel's own reconstruction of how extrapolation works in the
biomedical sciences is called *comparative process tracing*. He
assumes that causes *C* (such as medical interventions or the
ingestion of toxic substances) bring about their effects *E*
(such as the appearance of symptoms or improvements or deteriorations
of symptoms) through a series of steps or stages. To trace a causal
process means to investigate through what set of stages *C*
brings about *E*. Process tracing is comparative when the set
of stages through which *C* brings about *E* in one
species or population is compared to the set through which it does so
(if it does so indeed) in another.



Comparative process tracing would be futile if, in order to know that
*C* causes *E* in the target species or population, we
would have to compare *all* the stages of the process between
model and target. This is because in order to do so, we would have to
know all stages of the process through which *C* causes
*E*, but if we did, we would already know that *C*
causes *E*. This brings us back to the extrapolator's
circle. Steel now argues that comparative process tracing avoids the
extrapolator's circle by demanding processes to be compared only
at stages where they are likely to differ and assuming that
differences between model and target matter only to stages that are
downstream from where they obtain. Thus, if we compare an intermediate
stage of the process which obtains in the model with that stage in the
target and find them to be relevantly similar, then the only
differences that may still obtain will be downstream from this stage.
We therefore do not require knowledge of the entire process from
*C* to *E* in the target, and the extrapolator's
circle is successfully avoided.



How useful comparative process tracing is as a method for
extrapolation for the biomedical sciences depends on how reliable the
assumption that only downstream differences matter to extrapolation
is, the reliability with which stages where there might be differences
between model and target can be identified and the reliability of our
mechanistic knowledge more generally. If, say, our reasons for
supposing that *C* causes *E* through a series of stages
*X*, *Y*, *Z* in the model, or that *X*
and *Z* are the stages where model and target are likely to
differ, are not very strong, then the method does not get off the
ground. This is an issue that depends on the quality of the existing
knowledge about a given case and cannot be addressed for the
biomedical sciences as a whole. There are certainly some examples of
well-established causal claims where it is known only that *C*
causes *E* but the details of the causal process are entirely
beyond our current grasp (Reiss forthcoming-a).



An alternative to comparative process tracing that has been
proposed is extrapolation by knowledge of *causal capacities*.
If *C* has a causal capacity to bring about *E*, then
*C* causes *E* in a somewhat stable or invariant manner.
Specifically, *C* will then continue to contribute to the
production of *E* even when disturbing factors are present
(Cartwright 1989). To establish that *C* has the causal
capacity to cause *E* therefore means to show that
*C*'s causing *E* is independent of the background
in which *C* and *E* occur to some extent. And
therefore, if *C* causes *E* in a model species or
population and *C* has the causal capacity to bring about
*E*, then there is some reason to believe that *C*
causes *E* also in the target species or population (for a
defense, see Cartwright 2011).



The usefulness of the method of extrapolation by causal capacities
depends, among other things, on the extent to which biomedical factors
can be characterized as having capacities. Many biomedical causes do
indeed have some degree of stability. The sickle cell trait is 50%
protective against mild clinical malaria, 75% protective against
admission to the hospital for malaria, and almost 90% protective
against severe or complicated malaria (Williams et al. 2005). These
figures suggest a reading along the lines of, 



in the presence of the sickle cell trait (a preventer of/disturbing
factor for malaria), infection with *Plasmodium malaria*
continues to affect outcomes consistently. (Reiss 2015b: 19)



But there is a high degree of interaction with other factors as well.
Whether or not a substance is toxic for an organism depends on minute
details of its metabolic system, and unless the conditions are just
right, the organism may not be affected by the substance at all. To
what extent this method will be successful therefore similarly
case-dependent as comparative process tracing.



As we can see, there is no general answer to the question whether or not
animal studies are valuable from a purely epistemic (as opposed to
ethical, economic, or combined) view. Other authors have developed a
practice-based taxonomy of animal modes to allow more accurate
assessment of the epistemic merits and shortcomings, and predictive
capacities of specific modeling practices (Degeling and Johnson 2013).
There is much evidence that species differ enormously with respect to
their susceptibility to have toxic reactions to substances. Thus,
while it is very likely that for any one toxin, there is some species
that is predictive of the human response, it is often hard to tell
which one is most appropriate for any particular toxin. A species that
predicts the human response well for one substance may be a bad model
for another. However, some authors suggest that extrapolations 
from animal models have been made successfully in at least some cases (Steel 2008
discusses the extrapolation of claims concerning the carcinogenicity
of aflatoxin from Fisher rats to humans; see Reiss 2010a for a
critical appraisal and Steel 2013 for a response).


## 7. Observational Studies and Case Reports



Frequently, in the biomedical sciences, reliable animal or other
non-human models are not available and RCTs on humans are infeasible
for ethical or practical reasons. In these and other cases, biomedical
hypotheses can be established using observational methods. As we have
seen in
 Section 5,
 evidence-based medicine regards observational methods as generally
less reliable than RCTs and other experimental methods. This is
because observational studies are subject to a host of confounders and
biases that can be controlled when the hypothesis is tested by
a--well-designed and well-conducted--RCT. But it is not the
case that observational methods cannot deliver reliable results. In
fact, it is well possible that the medical knowledge that has been
established observationally by far exceeds the knowledge that comes
from RCTs. Here are some examples of medical interventions that are
widely accepted as effective but whose effectiveness has not been
tested using RCTs: penicillin in the treatment of pneumonia, aspirin
for mild headache, diuretics for heart failure, appendectomy for acute
appendicitis and cholecystectomy for gallstone disease (Worrall 2007a:
986); automatic external defibrillation to start a stopped heart,
tracheostomy to open a blocked air passage, the Heimlich maneuver to
dislodge an obstruction in the breathing passages, rabies vaccines and
epinephrine in the treatment of anaphylactic shock (Howick 2011b,
40).



Observational studies often begin by reporting a recorded correlation
between a medical outcome of interest and one or a set of independent
variables: lung cancer rates are higher in groups of smokers than in
groups of non-smokers, liver cancer rates are higher in populations
that tend to consume food that has been contaminated with aflatoxin
than in populations whose food is uncontaminated, to give a few
examples. That smoking causes lung cancer, or aflatoxin cancer of the
liver, would indeed account for the observed correlations. But so
would a variety of other hypotheses. Generally, if two variables
*X* and *Y* are correlated, it may be the case that
*X* causes *Y*, *Y* causes *X* or a common
factor *Z* causes both *X* and *Y* (or a
combination of these). In the smoking/lung cancer case, all three
hypotheses were invoked as possible accounts of the data. Ronald
Fisher famously proposed that it may be the case that early stages of
bronchial carcinoma cause an individual to crave cigarettes, and he
provided some evidence that both smoking behavior and susceptibility
to lung cancer have a common genetic basis (Fisher 1958). Moreover, it
is possible that the correlation itself is spurious--that the
data are correlated as per some measure of correlation such as
Pearson's coefficient, but that the underlying variables are not
in fact correlated in the population of interest. Selection bias is
normally understood as the bias that obtains when individuals
self-select into the observed population and the reasons for which
they do so are correlated with the outcome variable. If an
observational study examines only hospitalized patients and smokers
are more likely to be in hospital for reasons that have nothing to do
with lung cancer, then smoking and lung cancer can be correlated in
the data even if the variables are independent in the general population.
Mismeasurement and diagnostic error provide another account of spurious correlation.
Suppose tuberculosis was on the rise a generation or so after many
people traded pipe smoking for cigarette smoking. Then, if it was
difficult to distinguish a death from tuberculosis from a death from
lung cancer because necropsy techniques were not sufficiently well
developed, the data might again show a correlation even though the
population variables are uncorrelated.



Retrospective observational studies work by ruling out alternative
hypotheses such as these *ex post* rather than controlling for
them *ex ante* as RCTs do (Reiss 2015a). In an RCT,
mismeasurement should not obtain because the protocol specifies
measurement procedures for the outcome variables in great detail in
advance. Selection bias should not obtain because patients are
randomized into treatment groups. Once allocated to a group, they are
prevented from obtaining another treatment elsewhere, and researchers
make sure that patients comply with the treatment regime. But there
are equivalent means to rule out these possibilities in observational
settings. While it may well be the case that early stages of cancer
cause a craving for cigarettes, this hypothesis cannot explain the
protective effect that stopping smoking has. At the time of the
smoking/lung cancer controversy in the mid-1950s, misdiagnosis was
indeed a problem. However, it could be shown that in order to account
for the observed rise in lung cancer incidence, the diagnostic error
at autopsy among older people would have to have been an order of magnitude 
higher than the diagnostic error among younger people (Gilliam 1955).
Mismeasurement could therefore also be ruled out. Similar
considerations helped to rule out other alternative hypotheses
(Cornfield et al. 1959).



Even if one were to believe, with the proponents of EBM, that
observational studies are generally less reliable than RCTs, medicine
could--fairly obviously--not do without them. There are
large numbers of pressing questions that could not be addressed by an
RCT for ethical, financial and other practical reasons. No-one would
seriously consider testing a proposition such as "Aflatoxin
causes cancer of the liver (in humans)" by an RCT. This is not
merely because of the straightforward ethical issues involved in
deliberately exposing humans to a potential carcinogen for the sake of
medical progress. It is also because exposure to low levels of aflatoxin can
take many years or even decades to produce symptoms. The ability of
researchers to control food intake in a large group of experimental
subjects for a very long has evident practical financial and
limitations. RCTs can also not be used when researchers or patients or
both cannot be blinded, and many medical interventions do require the
doctor's or the patient's knowledge of details about the
intervention.



Moreover, it is not clear that RCTs are always more reliable than
observational studies to answer questions both methods are able to
address. Whether or not a study is reliable depends on whether or not
confounders and biases have in fact been eliminated, not by which
method they have been eliminated. Issues concerning the reliability of
a method can be entangled with issues concerning its ability to
address the research question the biomedical scientist seeks to
answer. Both RCTs and observational studies in the biomedical sciences
are typically employed to test rather complex hypotheses about the
safety and efficacy of medical interventions. It may well be that some
of the issues are more reliably treated by one method and others by
the other.



A famous controversy in which the results from observational studies
and those from RCTs conflicted was that over the benefits and safety
of hormone replacement therapy (HRT) in the early 2000s (Vandenbroucke
2009). HRT seemed protective for coronary heart disease in
observational studies, whereas RCTs indicated an increase in the first
years of use. For breast cancer, combined hormone preparations showed
a smaller risk in an RCT than in observational studies. In the end it
turned out that the timescale of the effects was responsible, and that
because of the way they are typically run, observational studies got
some issues right and RCTs others:



> 
> 
> The observational studies had picked up a true signal for the women
> closer to menopause. In the randomised trial, that signal was diluted
> because fewer women close to menopause were enrolled... The
> randomised trials had it right for coronary heart disease but failed
> to sufficiently focus on women close to menopause for breast cancer.
> The main reasons for the discrepancies were changes of the effects of
> HRT over different times... (Vandenbroucke 2009: 1234)
> 
> 
> 



Case reports remain extremely popular in medicine both as publications
to communicate within the field and for pedagogical purposes. In
short, a case report describes a medical problem experienced by one or
more patient, usually involving the presentation of an illness or
similar that in some way difficult to explain or categorize based on
existing understandings of disease or understandings of physiology and
pathology. Cases in medicine take highly standardized forms of
presentation which are inculcated in health care professionals during
their education, and many have commented on their highly standardized
narrative structure and its epistemic and other implications (Hunter
1991; Hurwitz 2006). Cases typically provide details on the
presentation of the disease, diagnosis, treatment, and outcomes for
the patient, with a focus on practice-based observations and clinical
care (rather than the results of randomized controlled trials or other
experimental methodologies). One of the purposes of cases is to gather
detailed information including facts that may not be immediately
relevant, but that could prove to be (Ankeny 2011). Thus the
information contained in the case and the case itself can be useful
over the long term particularly if it can be systematically combined
with other cases into larger datasets.



Single cases are seen by some as problematic as a form of evidence
particularly in the era of EBM, because they often focus on highly
unusual manifestations of illness and disease, rather than typical or
repeatedly observed conditions that might support generalizable rules.
This feature has led some to describe medicine as a "science of
particulars" (Gorovitz and MacIntyre 1976), or as an art rather
than a science (Pellegrino 1979), particularly in processes of
diagnosis (see
 Section 9).
 However standard accounts of EBM include the case series as a type of
evidence, which involves the aggregation of individual cases of
patients with similar attributes (e.g., who received the same
treatment or therapy) who are tracked over time using descriptive data
and without utilizing particular hypotheses to look for evidence of
cause and effect. EBM does place the case series quite low in its
hierarchy of evidence but nonetheless it is acknowledged that cases
have potential usefulness especially where forms of evidence that rate
more highly are not available, as may often be the case where human
patients are concerned due to practical or ethical reasons, or where
the available evidence at higher levels has been produced in a manner
that is methodologically or otherwise flawed.



Cases can serve other purposes: for instance analyses of cases can
provide working hypotheses about casual attribution that can ground
further tests of causal relations (Ankeny 2014), which in turn allows
use of more traditional methodologies such as RCTs, cohort studies,
and so on to explore these causal hypotheses. In the context of
clinical care, cases can allow health care providers to identify a
cause that can be manipulated to cure (or prevent) the condition in
question, in order to treat ill patients, even in the absence of more
rigorous forms of evidence.


## 8. Diagnosis



Diagnosis is the process through which a clinician determines what is
wrong with a patient who is ill or ailing in some way. Although a
critical part of the practice of medicine, it has been relatively
neglected in the literature of philosophy of medicine particularly in
comparison to more statistically-based methods for evaluating evidence
in other fields (Stanley and Campos 2013). The key philosophical
issues that arise in this context relate to how such determinations
can be made in a manner that is accurate given the high amount of
uncertainty and complexity often associated with the human condition,
and hence involve logical, epistemological, and ontological issues.
The usual way of proceeding in a clinical setting is to ask the
patient to articulate what is ailing him or her, and thus to use a
standardized reporting format to detail various symptoms which
represent subjective manifestations of the illness or disease. In
addition, clinicians perform various tests and examinations that allow
more objective manifestations or signs to be recorded, such as heart
rate, blood pressure and count, reflexes, and so on. A perennial
debate in the philosophy of medicine is what constitutes symptoms and
signs and whether they are in fact distinct, which relates to deeper
issues about the realism of disease conditions as discussed above
 (Section 2).



The tricky part of the process is to find a means for mapping these
symptoms and signs onto a particular disease condition. Some would
advocate that this process is no different than usual methods in
philosophy of science for hypothesis generation and testing based on
evidence, and this type of model fits with what is termed differential
diagnosis. Differential diagnosis involves a set of hypothetical
explanations for a particular condition which come to be ruled in (or
out) based on the evidence together with additional data that is
collected, hence relying on a form of reasoning via decision nodes or
algorithmic pathways (Stanley and Campos 2013). However, the details
of the rules of reasoning that underlie this sort of process remain
largely unarticulated, as does the amount of "tacit"
knowledge that may contribute to diagnostic reasoning.



There are various ways in which diagnosis is taught and
operationalized in clinical settings: in some subspecialties in
particular, "pattern" recognition often using pictorial
representations seems to be common, and hence diagnosis is a form of
recognizing repeated patterns. However this approach can be dangerous
particularly among novices, given the large number of similar patterns
among common diseases. Some have claimed that the making of a
diagnosis is both a deontic act and computable, and that diagnoses are
relative only inasmuch as they occur in a complex context which in
turn makes them a social practice (Sadegh-Zadeh 2011).
Computer-assisted diagnostic techniques have improved and are used
increasingly in clinical settings; Kenneth Schaffner (1981) provided
an early analysis of the criteria which an ideal diagnostic logic
would need to satisfy (for updated discussions see Schaffner 1993,
2010, and for arguments about the limitations of such types of
diagnosis see Wartofsky 1986). In recent years there is a relative
consensus among medical professionals and those involved in medical
informatics that medical diagnosis almost certainly relies on some
form of "fuzzy logic" (e.g., Sadegh-Zadeh 2000; Barro and
Marin 2002).


## 9. Clinician Judgement and the Role of Expertise



As we have seen in
 Section 5,
 hierarchies of evidence in evidence-based medicine rank study results
from "systematic" clinical research such as RCTs and
observational studies higher than "unsystematic" expert
opinion. The epidemiologists who initiated the formal EBM movement in
the early 1990s had good reason to be skeptical about expert opinion.
When therapies are subjected to systematic tests, tradition and expert
opinion are sometimes shown to be flawed. John Worrall discusses three
examples: grommets for glue ear, ventricular ectopic beats repressing
substances such as encainide or flecainide for cardiac arrest, and
routine fetal heart rate monitoring to prevent infant death (Worrall
2007a: 985). In each case we have a procedure the effectiveness of which is
indicated by common sense and knowledge about the patho-physiological
pathways--glue ear is a condition produced by a build-up of fluid
in the middle ear that is unable to drain away because of pressure
differentials, grommets act by letting air into the middle ear and
thereby equalizing pressure, for instance--but which, when tested
by a randomized trial, turns out to be ineffective at best and
positively harmful in the worst case.



Misjudgments concerning the efficacy of therapies for purely epistemic
reasons are not the only worry that one might have about expert
opinion. Medical experts and patients are in what economists call a
principal-agent relationship. The principal--in this case, the
patient--desires the delivery of a certain good or
service--in this case, his health. He instructs an agent--in
this case, the doctor--with it, because he lacks the expertise to
produce the good himself. The good can only be produced with
uncertainty: no therapy is 100% effective. Moreover, the success at
delivering the good depends in part on the agent's effort. The
doctor may not always choose the optimal therapy for a patient (we can
suppose that it takes some effort to select the optimal therapy for a
patient), and any therapy can be implemented sloppily. Moreover,
lacking expertise, the patient cannot observe the level of effort a
doctor puts in. He therefore cannot design a contract that makes
payments dependent on level of effort (much less on success, as
success is in part influenced by factors outside of either
party's control). Agents therefore have an incentive to cheat:
not to put in the level of effort required to select and deliver the
optimal therapy from a patient's point of view.



If patients and doctors were perfectly rational and motivated only by
their own material welfare, and in the absence of regulation, there
simply wouldn't be a market for health services. Doctors would
choose therapies that are best for them and not for patients, and
patients would anticipate this behavior and stop seeking
doctor's services in the first place. In our world, neither
patients nor doctors are particularly rational, nor are they motivated
purely by self-interest, there are ethical codes such as the modern
form of the Hippocratic Oath, and the health sector is one of the most
regulated industries of all. All this does not, however, change the
incentive structure in which doctors and other providers of health
services operate. Because they and not the patients are experts, they
have incentives to choose therapies that are in their best interests
and not in the patients' interests.



There is a further complication. Many, probably most, doctors have
connections to the pharmaceutical industry in one form or another.
According to one study, 94% of U.S. physicians receive financial
benefits from the pharmaceutical industry (Bekelman et al. 2003). Even
if we suppose that doctors do not prescribe a therapy because they are
paid to do so, marketing efforts directed at them will influence
treatment recommendations, if only because they know certain pills
better than others, or because some treatments are at the top of their
heads.



For all these reasons, the EBM principle that treatment decisions
should be based on the best available evidence from systematic
research does not come out of nowhere. If, say, there is an RCT or an
observational study that reports that treatment *X* is more
effective at relieving symptoms *S* than treatment *Y*,
it would seem bad to recommend a patient who suffers from *S*
to take *Y* because his GP doesn't know about *X*,
doesn't know the study result, personally profits from
prescribing *Y* or is inattentive. However, while these are all
bad reasons to recommend *Y* over *X* in the light of
the study result, there may be a variety of good reasons.



As discussed in
 Section 5,
 RCTs and many observational studies are population-level studies,
which produce average results that are not straightforwardly
applicable to individuals. If, say, treatment *X* reduces the
risk of suffering from some adverse event over a period of time by 50%
in population *p*, that is, the risk ratio (RR) for this
treatment is 50%, then there may be no individual in *p* for
whom the treatment halves the risk. Instead, the RR may vary
dramatically among the subpopulations of *p*, and it may well
be the case that *Y* is more effective than *X* for some
subpopulations.



The same is true of side effects. Tonelli (2006) discusses a case
where a patient who suffers from multiple sclerosis receives a
treatment that does seem to alleviate her symptoms, but since she has
started taking it, she has been plagued by severe episodes of
depression. Clinical trial results indicate that the drug is effective
in treating multiple sclerosis, and no adverse psychiatric effects
have been reported. Her GP and her psychiatrist now debate whether to
continue the treatment. There are various reasons why the clinical
studies do not show evidence of mental health effects: the trial
subjects weren't properly screened for depression; adverse
effects were found but not reported; the adverse effects were not
statistically significant--but they may have been clinically
significant for some subpopulations; the side effects only obtain in
populations that differ from the trial populations.



This case shows that a treatment's effectiveness in relieving
the symptoms of the disease for which it was prescribed is not the
only consideration when making a treatment decision. The goal of the
treatment is to improve the patient's wellbeing, which is well
recognized by the proponents of EBM. A patient's wellbeing has
many components, of course, and the symptoms of any given disease are
at best one element in its determination. This is another reason why
clinical judgment must be exercised in the derivation of a treatment
recommendation.



Unfortunately, experts--like all humans--are notoriously bad
decision makers. Cognitive psychologists have established a large
number of cognitive biases to which human experts are subject: they
suffer from overconfidence (e.g., Dawes and Mulford 1996) and
hindsight bias (e.g., Fischhoff 1975; Hugh and Dekker 2009); are
regularly outperformed by simple mechanical algorithms (e.g., Grove
and Meehl 1996); commit the conjunction fallacy (Tversky and Kahneman
1983; Rao 2009), and many others.



To give an example for a simple mechanical algorithm outperforming
experts, consider the Goldberg Rule, according to which a patient is
to be qualified as neurotic if \(x = (\textrm{L} + \textrm{Pa}+
\textrm{Sc}) - (\textrm{Hy} + \textrm{Pt}) > 45\) (where L is a
validity scale and Pa, Sc, Hy, and Pt are clinical scales of a
Minnesota Multiphasic Personality Inventory or MMPI test) and as
psychotic otherwise. Lewis Goldberg tested the rule on a set of MMPI
profiles from 861 patients who had been diagnosed by the psychiatric
staff in their hospital or clinic and found it to be 70% accurate;
clinicians' accuracy ranged from 55% to 67% (Goldberg 1968; for
a discussion, see Bishop and Trout 2005).



There is no one strategy to deal with the various biases and interests
that affect clinicians' judgments. Better numeracy and
statistical training at universities can help to eliminate some
cognitive biases (Gigerenzer 2014). Computer-aided medical diagnosis and decision making
may alleviate others. No training or computer program can make
normative judgments, however, and neither will help with adverse
incentive structures and financial interests. These difficulties also
beset *committees* of medical experts to which we are turning
next.


## 10. How Are Collective Expert Judgments Made in Medicine?



One way to help overcome expert bias is by making medical decisions
not dependent on individual expert judgments but instead have groups
of experts coming to some form of aggregate judgment. The U.S.
National Institutes of Health, for instance, used to organize
so-called consensus conferences designed to resolve scientific
controversy. Panel members are chosen from clinicians, researchers,
methodologists and the general public. Federal employees are not
eligible, nor are researchers who have published on the subject at
hand or have financial conflicts of interest (Solomon 2007). These
exclusions are intended to contribute to controlling government
influences as well as any biases due to financial or intellectual
interests.



Consensus conferences and other mechanisms for reaching group
judgments are clearly no panacea. Miriam Solomon (2015), for instance,
argues that consensus conferences tend to "miss the window of
epistemic opportunity" in that they often take place after the
medical community has already settled an issue. More important in the
present context is the observation that while these conferences
possibly help to control some forms of partiality, they are
ineffective in reducing others and may be responsible for the
introduction of new biases. One concern is that panel members may read
the existing evidence selectively, for instance, because of weighing
salient studies or studies that are available to them more heavily.
Another is that phenomena such as groupthink (Janis 1982) and peer
pressure may influence results. In an NIH consensus conference panel
members have to come to a verdict after only two days of hearings and
deliberations. Under these conditions it is certainly possible that
more outspoken panel members or those who perform well under extreme
pressure have a undue influence on results. Moreover, it is not clear
that excluding clinicians who have published on the issue at hand is
always such a good idea. After all, it is not implausible to maintain
that those scientists who actively work on a research topic are those
who best understand it and therefore can make the best informed
judgments. For these and other reasons, Solomon (2007, 2015) explores
the consequences of judgment aggregation. In this process group
members typically do not deliberate but instead cast their opinions
which are then aggregated using some pre-determined procedure. The
majority rule would be a simple example of such a procedure.



Coming to a group judgment using a mechanical procedure such as
majority vote has a number of advantages. First, there are epistemic
advantages that can be illustrated by Condorcet's Jury Theorem.
This theorem shows that if (a) the judgment concerns a proposition
that can either be true or false, (b) jury members have an independent
probability \(>.5\) that they get the judgment right, (c) the
individual judgments are aggregated using majority vote, then the
larger the jury, the more likely it is to reach the correct group
judgment. Under these conditions, then, a committee of experts is
likely to make a better judgment than a single expert. Moreover, in
the absence of deliberation and pressure to come to a unanimous
results, and when voting is secret, the influence of groupthink, peer
pressure etc. is attenuated or eliminated.



When conditions (a)-(c) do not hold, results are more ambiguous
or even negative. When experts are not reliable, i.e., the individual
probability of getting the judgment right is \(<.5\), the larger
the group, the *less* likely is it to reach a correct group
judgment and the optimal group size is a single expert. When the
outcome can have more than two values, inconsistent results can
obtain. This can easily be demonstrated with an example in which there
are three possible outcomes and three experts. Suppose, for instance,
that a panel has to decide which of three treatments *A*,
*B* and *C* is the most effective in treating some disease.
The individual panel members have the following individual
rankings:




Expert I: \(A > B > C\)



Expert II: \(B > C > A\)



Expert III: \(C > A > B\),




where ">" means "more effective". There is
now a majority that holds that *A* is more effective than
*B* (I&III), a majority that holds that B is more effective
than *C* (I&II) and a majority that holds that *C* is
more effective than *A* (II&III). More generally, whenever
there are logical relations among the propositions to be decided (in
this case: \(A > B\) and \(B > C\) implies that \(A > C\)),
there are at least three panel members, and votes are aggregated by
the majority rule, inconsistencies can arise at the group level
(Pettit 2001).



The majority rule is of course only one way to aggregate judgments.
The Delphi method (e.g., Dalkey and Helmer 1963; for an application to
medicine, see Jones and Hunter 1995) applies to cases where the task
is to provide a numerical estimate of some variable of interest (say,
the risk difference a new treatment makes). Experts answer
questionnaires in several rounds. After each round, a facilitator
provides an anonymous summary of the experts' estimates from the
previous round and the reasons given for their judgments. Experts are
thus supposed to be encouraged to revise their earlier answers in
light of other experts' estimates and justifications. During
this process the range of the estimate will often decrease, and it is
hoped that the group will converge towards the correct answer. The
process is stopped after a pre-determined stopping criterion such as
number of rounds, achievement of consensus, stability of results, and
an average of the estimates of the final round is used as result.



Solomon (2011, 2015) raises a fundamental issue concerning group
judgments that is entirely independent of the specific method used. She argues that we
do not often find group judgment methods to determine the truth of
scientific hypotheses or estimates of variables in the natural
sciences (though see Staley 2004). If there is uncertainty about, say, which of two alternative
hypotheses is true or what value a natural constant has, scientists go
out and test, experiment, measure. Controversies, in other words, are
settled on the basis of evidence, not (individual or group) opinion.
Shouldn't we, with the advancement of evidence-based medicine,
expect the same to happen in medicine? Consequently, she recommends
more widespread use of mechanical techniques for amalgamating evidence
such as meta-analysis in lieu of consensus conferences and the
like.



The frequency of NIH consensus conferences has indeed markedly
declined in recent years (Solomon 2011, 2015). But this is
of course no reason to maintain that group judgments are no longer
needed. Consensus conferences may be the wrong tool for the purposes
of the NIH, or the NIH may have a mistaken view about the ability of
evidence to settle disputes adequately. Indeed, there are at least two
reasons to believe that group judgment procedures are here to
stay.



The first reason is that, as we have seen above, medical decisions are
always in part decisions about normative matters. No treatment is
entirely without side effects and so if judgments about efficacy are
to be of practical guidance, they must include a weighing of benefit
(alleviation of disease symptoms) against cost (suffering from side
effects)--even if *economic* costs and benefits are not to
be taken into consideration. Second, government agencies such as the
U.S. Food and Drug Administration (FDA) have to decide whether new
treatments should be licensed to be marketed. These decisions often
have significant consequences, and democracies tend to prefer to be
able to hold someone accountable for making them. Drug approval
therefore cannot be determined on the basis of evidence according to
some mechanical algorithm.



Biddle (2007) discusses epistemological and moral issues of drug
approval in the context of a case study on Vioxx, an analgesic. Vioxx
was approved by the FDA in 1999 but five years later pulled from the
market by its manufacturer Merck due to safety concerns. It is
estimated that some 55,000 people died from taking the drug (Harris
2005). Biddle observes that the FDA is not sufficiently independent of
the pharmaceutical industry to make unbiased decisions likely. Many of
the members of the FDA's drug approval committees have financial
conflicts of interests (often in the form of receiving benefits from
the pharmaceutical company whose drug is to be approved), and a large
number of employees of the FDA are dependent on the "user
fees" industry pays to help cover the cost of drug approval. To
solve these problems of conflicts of interests, Biddle proposes to
institute an adversarial system in which two groups of advocates, a
group of representatives of the manufacturer and a group of
independent scientists, would argue before a panel of judges over
whether a drug should be allowed on the market. The panel of judges in
this model also consists of independent FDA or university scientists.
He argues that the adversarial system would better acknowledge the
fact that an increasing number of medical researchers have financial
ties to the pharmaceutical industry by treating them as advocates
rather than disinterested experts. (See also Reiss and Wieten 2015, Reiss forthcoming-b.)


## 11. Values in Medical Research



There is no doubt that medical research is shaped by various external
values, in ways similar to the value ladeness that is well-recognized
in other areas of science (see entry on
 scientific objectivity).
 Many of these values create a variety of ethical dilemmas relating to
equity of access to health care and similar. Even in recent years once
medical research has been made more inclusive, this trend has
introduced a host of additional philosophical and ethical issues
(Epstein 2007). For our purposes, we will focus on the implications of
the systematic exclusion of certain types of individuals, groups, or
diseases from research for future research as well as clinical medical
practice in terms of the validity of evidence produced and decisions
made based on that evidence. 



In traditional medical research, it was generally assumed that white
male participants could be used as the basis of generalizations that
in turn could be extrapolated to all other populations, including
minorities and females (Dresser 1992). Reviews of the literature
indicate that women in particular have been excluded (especially older
women), and that research on women has usually been related to
reproductive function and capacity (Inborn and Whittle 2001). Such
types of research have been argued to fail the ideals of quality
medical research as well as evidence-based health care (Dodds 2008).
Although some improvements have been made in recent years, there
remain certain forms of blanket exclusions for instance of women of
childbearing age or pregnant women in many types of medical research.
These types of systematic exclusion are highly problematic especially
because there is clear evidence of critical differences between men
and women with regard to a range of factors relating to receptivity to
therapies for both biological and social reasons. 



In the case of minorities such as African-Americans in the United
States, even when research trials seek to recruit them, a range of
factors may contribute to them not being involved in medical and other
types of research studies. These include distrust due to historical
and institutional racism including research performed without consent;
lack of understanding about research and consent; social stigma;
financial considerations; and lack of culturally-sensitive recruitment
methods by researchers (e.g., Huang and Coker 2010). Such gaps in
medical research potentially lead to use of treatments or therapies
that may in fact be harmful for particular groups, and may result in
the withholding of therapies that might be beneficial.



Medical research also is affected by which conditions or diseases are
selected for investigation (Reiss and Kitcher 2008): perhaps most
notoriously, "orphan" diseases which are either rare,
common only in minority populations, or only present in certain
developing world or other lower socioeconomic settings, are often
neglected for drug and other therapy development because it is
perceived that there will not be a viable commercial market for any
products on which research might be done due to the at-risk or
affected population (and hence such potential products are often
termed "orphan drugs"). In some cases patients may pursue
"off-label" use of drugs which are approved for a
condition other than the one they have, because approval for an
"orphan" disease is unlikely due to cost and demand;
however such off-label uses of drugs even when overseen by a physician
typically result in lack of consistent collection of evidence and
absence of typical risk-benefit regulatory considerations utilized
when a drug is approved for particular purposes.



A final way in which our knowledge in medicine generated by research
is potentially adversely affected by values is through the funding
patterns connected with research. As implied above, pharmaceutical
companies sponsor a considerable portion of drug trials and have a
variety of interests at stake in these investments well beyond the
gathering of evidence for the effectiveness (or lack thereof) of a
particular product. There is consistent evidence that negative
research results typically are suppressed when sponsored by industry
(Lexchin 2012a), leading to a bias in what is reported and thus what
evidence is available on which to make prescribing and treatment
decisions. Bias also has been found in a number of other areas: within
the study itself in the choice of research question or topic of
investigation, in the choice of doses or drugs against which the drug
under study is to be compared, in the control over trial design and
various changes in protocols, and in decisions to terminate clinical
trials early, and in the reinterpretation of data, as well as in the
publication of data such as restrictions on publication rights, use of
fake journals, favoring journal supplements and symposia rather than
peer review venues, the use of ghostwriting, and in the details of the
reporting of results and outcomes (Sismondo 2008; Reiss 2010b; Lexchin 2012b). All of these
issues weaken the evidence base on which clinical care judgments are
made, and also lead to potentially adverse effects for patients.


## 12. Measuring Medical Outcomes



In order to evaluate medical outcomes quantitatively, they have to be
measured. There are numerous reasons for aiming to quantify medical
outcomes. We may want to compare two or more treatments with respect
to their efficacy at relieving certain symptoms or their ability to
prevent deaths due to a certain disease. When resources are scarce, we
may not only want to invest in treatments that are efficacious (that
is, they do improve patient morbidity, mortality or both) but also
efficient (that is, it is more efficacious than other treatments
relative to the cost of procuring it). For matters of international
comparison, development and international justice, we also want to
have measures of *disease burden*: Which of a number of
tropical diseases has the highest cost in terms of increased morbidity
and mortality? For each research dollar spent on treatments for
disease *X*, how much can we expect to reduce the morbidity and
mortality it causes?



Clinical trials now often report so-called patient-reported outcome
measures or PROMs. A PROM is a questionnaire given to patients to
evaluate certain aspects of their quality of life, functioning or
health status after a medical intervention without interpretation of
the patient's response by a clinician or other people. It might
ask, for example, how difficult patients find it to climb up a flight
of stairs after hip surgery or whether or not a cancer treatment helps
them to pursue their hobbies. The main goal of a PROM is the
assessment of treatment benefit or risk in cases where the medical
outcome is best known by the patient or best measured from the patient
perspective.



PROMs can vary considerably in length and complexity depending on the
concept that is being measured. In simple, straightforward cases
(e.g., intensity of a certain kind of pain), a single question might
suffice. In others, it may be necessary to address several aspects of
a more complex functioning with a number of questions each. Either
way, the design of the questionnaire should make sure that the
instrument reliably measures the concept of interest. The FDA
distinguishes the following six measurement properties or
"tests" (FDA 2009: 11):


* Test-retest or intra-interviewer reliability ("Are the
scores stable over time when no change is expected in the concept of
interest?")
* Internal consistency ("Is there a high correlation between
the responses that purport to measure the same concept?")
* Inter-interviewer reliability ("Is there agreement among
responses when the PROM is administered by two or more different
interviewers?")
* Content validity ("Is there evidence that the instrument
measures the concept of interest?")
* Construct validity ("Is there evidence that the
relationships between responses conform to expectations?")
* Ability to detect change ("Is there evidence that the
instrument can identify differences in scores over time in individuals
or groups who have changed with respect to the concept of
interest?").



Despite their plausibility, these tests are not methodologically
innocuous. Content validity, for instance, is assessed on the basis of
qualitative research in the form of patient interviews, focus groups,
and qualitative cognitive interviewing (the latter refers to a method
that asks respondents to think aloud and describe their thought
processes as they answer the instrument questions and involves
follow-up questions in a field test interview to gain a better
understanding of how patients interpret questions). This qualitative
research aims to develop questions with standardized meanings that are
shared between patients and clinicians. Arguably, however, there will
always be differences in interpreting phrases such as "bodily
pain" or "difficulty in lifting one"s arm'
because they refer to a patient's experiences, and these will
differ from patient to patient and, in a given patient, from time to
time (Rapkin and Schwartz 2004). Moreover, there may be good
philosophical reasons to allow for the expression of a sufficient
array of legitimate perspectives on health and quality of life instead
of insisting on a standardization of meaning across patients and
contexts (McClimans 2010). Similarly, internal consistency can be
desirable only to the extent that the concept is a relatively simple
one and different questions really do address the same concept. It is
of less relevance when the disorder is heterogeneous (McClimans and
Browne 2011). These kinds of worries can be raised with respect to
each of the measurement tests. Finally, there is an issue when several
PROMs that address a given disorder or treatment exist. Different
PROMs will score differently with respect to the different tests, and
there is no universally valid schema to weigh their relative
importance (*ibid*.).



Disability-adjusted life years or DALYs aim to measure burden of
disease. The measure was originally developed by Harvard University
for the World Bank and World Health Organization (WHO) in 1990 and is
now widely used by heath policy researchers for comparisons between
countries and over time and as a tool for policy making. It can also
be used to measure the effectiveness of interventions, though these
are usually health policy rather than medical interventions
narrowly construed. The WHO makes regular global disease burden
estimates in terms of DALYs at regional and global level for more than
135 causes of disease and injury (Mathers et al. 2002).



The principal idea behind DALYs is simple. If a woman in Guatemala
dies of Chagas disease at age 63, this adds 20 DALYs to the global
disease burden because her death is 20 years "premature"
as compared to Japanese life expectancy (which is taken as the
standard because it is the highest world wide). If a man in
Hamburg has an accident that confines him to the wheelchair for the
rest of his life, this contributes 0.57 DALYs for each of his
remaining life years because the weight for paraplegia is 0.57. Every
kind of disease or impairment is thus given a number between 0 and 1
(where 0 = full health and 1 = death) that makes it comparable to
other conditions. For example, blindness has a weight of 0.43. As
blindness contributes less to the burden of disease than paraplegia,
this means that blindness is regarded as the less severe of the two in
terms of its reduction of functional capacity
(Pruss-Ustun et al. 2003).



The simple idea is complicated by two adjustments, however. Typical
burden of disease studies weigh an impairment differently depending on
the age of the person whose functional capacity is impaired by the
disease or disability. Blindness, say, has a greater impact on the
burden of disease if it occurs at age 20 than if it occurs at very
young or older ages (Pruss-Ustun et al. 2003).
Moreover, if the man who has the accident now can be expected to live
with the disease for 30 years, future years of disability are
discounted by a factor. The further into the future a disability
occurs, the less it contributes to the disease burden
(*ibid*.).



The adequacy of any socio-economic indicator has to be evaluated in
the light of the purpose that it is meant to serve (Reiss 2008). If
DALYs are supposed to measure the everyday concept "burden of
disease", we may criticize, for instance, that the indicator
fails to take account of societal, cultural, climatic and other
variations within which the disease or disability occurs. Being
paraplegic, say, is less burdensome when it occurs in societies that
spend more resources on making public buildings and transport
wheelchair accessible, that display more tolerance towards the
handicapped, or in flatter than in hillier regions. Arguably,
therefore, DALYs measure ill-health rather than disease burden (Anand
and Hanson 1997). Similarly, because ill-health is measured as a
percentage, a disease occurring in a person who is already handicapped
contributes less to the measure than the same disease occurring in an
otherwise comparable but not handicapped person. If DALYs are used to
make public health decisions, however, it might be better to
prioritize those individuals who are least well off instead of those
who are relatively better off (*ibid*.).



The WHO is very explicit that numerous choices made in the
construction of the DALY measure are value-based (Murray 1994;
Pruss-Ustun et al. 2003). Clearly, there is no matter
of fact whether paraplegia constitutes a more severe impairment of
someone's functional capacity than blindness, much less the
precise extent to which it contributes to the burden of disease. The
same is true of the duration of the time lost due to premature death,
age weights and time preference. While any given choice will, due to
its value-laden nature, be controversial, the WHO makes some efforts
to represent societal preferences instead of, say, *a priori*
philosophical arguments. For example, the disability weights used in
the 2003 World Health Survey were based on health state valuations
from large representative population samples in over 70 countries
(Pruss-Ustun et al. 2003: Ch. 3). Similarly, age
weights are based on empirical studies that have indicated there is a
broad social preference to value a year lived by a young adult more
highly than a year lived by a young child, or lived at older ages
(Murray 1996).