title: Measurement of health status. Ascertaining the minimal clinically important difference. Interpreting changes in quality-of-life score in N of 1 randomized trials. Surveying physicians to determine the minimal important difference: implications for sample-size calculation. A combination of distribution- and anchor-based approaches determined minimally important differences (MIDs) for four endpoints in a breast cancer scale. Three methods for minimally important difference: no relationship was found with the net proportion of patients improving. Effect Sizes for Interpreting Changes in Health Status Individualizing quality-of-life outcomes reporting: how localized prostate cancer treatments affect patients with different levels of baseline urinary, bowel, and sexual function. What is a clinically meaningful change on the Functional Assessment of Cancer Therapy-Lung (FACT-L) Questionnaire? Results from Eastern Cooperative Oncology Group (ECOG) Study 5592. Sufficiently Important Difference: Expanding the Framework of Clinical Significance
abstract: In recent years quality of life instruments have been featured as primary outcomes in many r and omized trials . One of the challenges facing the investigator using such measures is determining the significance of any differences observed , and communicating that significance to clinicians who will be applying the trial results . We have developed an approach to elucidating the significance of changes in score in quality of life instruments by comparing them to global ratings of change . Using this approach we have established a plausible range within which the minimal clinical ly important difference ( MCID ) falls . In three studies in which instruments measuring  dyspnea , fatigue , and emotional function in patients with chronic heart and lung disease were applied the MCID was represented by mean change in score of approximately 0.5 per item , when responses were presented on a seven point Likert scale . Furthermore , we have established ranges for changes in question naire scores that correspond to moderate and large changes in the domains of interest . This information will be useful in interpreting question naire scores , both in individuals and in groups of patients participating in controlled trials , and in the planning of new trials To provide additional evidence regarding the plausible range of the differences in health-related quality of life ( HRQL ) question naire scores within which the minimal important difference ( MID ) falls , we review ed the results of 32 r and omized controlled trials in individual subjects ( N of 1 RCTs ) with chronic diseases . These trials had been conducted to establish whether a patient was obtaining more good than harm from a medication . Each N of 1 RCT included a series of pairs of treatment periods , one period on active drug , and one on placebo or alternative drug . We examined the relationship between small ( MID ) , medium , and large differences between periods within pairs , as indicated by Global Ratings and differences between these same periods according to HRQL question naires . The results showed a mean difference of 0.29 points per question in HRQL question naire scores corresponded to the MID . Differences of approximately 0.66 points per question corresponded to a moderate difference as ranked by the Global Rating ; difference of about 1.09 points per question represented marked difference The minimal important difference ( MID ) is the smallest benefit of treatment that would result in clinicians recommending it to their patients . The MID is necessary to calculate sample size for r and omized clinical trials , but its chosen value is often arbitrary . This study set out to determine the practicability of surveying physicians to elicit the MID for clinical trial sample -size calculation . Using a mail survey , we elicited the MID of different physician specialties ( family medicine , internal medicine , vascular surgery ) for using propranolol  to slow abdominal aortic aneurysm ( AAA ) growth assuming that propranolol was efficacious in this condition . We used different outcome measures ( growth rate or proportion of patients requiring surgery ) and different methods of data presentation for the proportion of patients requiring surgery ( absolute risk reduction or number needed to treat ) . The MID varied significantly by physician specialty , experience with AAA and propranolol , and the method used to elicit the MID . Consequently , sample -size calculations using these various MIDs varied from 116 to 3015 . Future attempts to elicit the MID need to consider carefully who is surveyed , how data are presented , and how opinions are elicited OBJECTIVE To determine distribution- and anchor-based minimal important difference ( MID ) estimates for four scores from the Functional Assessment of Cancer Therapy-Breast ( FACT-B ) : the breast cancer subscale ( BCS ) , Trial Outcome Index ( TOI ) , FACT-G ( the general version ) , and FACT-B. STUDY DESIGN AND SETTING We used data from a Phase III clinical trial in metastatic breast cancer ( ECOG study 1193 ; n=739 ) and a prospect i ve observational study of pain in metastatic breast cancer ( n=129 ) . One third and one half of the st and ard deviation and 1 st and ard error of measurement were used as distribution-based criteria . Clinical indicators used to determine anchor-based differences included ECOG performance status , current pain , and response to treatment . RESULTS FACT-B scores were responsive to performance status and pain anchors , but not to treatment response . By combining the results of distribution- and anchor-based methods , MID estimates were obtained : BCS=2 - 3 points , TOI=5 - 6 points , FACT-G=5 - 6 points , and FACT-B=7 - 8 points . CONCLUSION Distribution- and anchor-based estimates of the MID do show convergence . These estimates can be used in combination with other measures of efficacy to determine meaningful benefit and provide a basis for sample size estimation in clinical trials OBJECTIVE To determine the impact on a responder type analysis of using three published methods to obtain the minimally important difference ( MID ) on the conclusion of a r and omized controlled trial ( RCT ) . STUDY DESIGN AND SETTING Using data from an RCT of supportive-expressive group therapy ( SEGT-intervention ) vs. st and ard care ( control ) in women with metastatic breast cancer , we measured individual responsiveness to change according to three levels of predefined MID ( 0.2 SD , 0.5 SD , and 1 st and ard error of measurement ) of the following six vali date d question naires : Profile of Mood States , Impact of Event Scale , Psychosocial Adjustment to Illness Scale , EORTC Quality -of-Life Question naire Core-30 , Mental Adjustment to Cancer , and a pain visual analog scale . The proportion of women improved by SEGT  and the number needed to treat according to three levels of MID were calculated . RESULTS There was no consistent difference in the net proportion of women improving with the SEGT vs. control arm according to the three different levels of MID . CONCLUSION The choice between different levels of distribution-based MID did not make an important difference in the net proportion of women improving with the SEGT . Future research should compare MID derived from clinical anchors , in particular patient opinions Health status measures are being used with increasing frequency in clinical research . Up to now the emphasis has been on the reliability and validity of these measures . Less attention has been given to the sensitivity of these measures for detecting clinical change . As health status measures are applied more frequently in the clinical setting , we need a useful way to estimate and communicate whether particular changes in health status are clinical ly relevant . This report considers effect sizes as a useful way to interpret changes in health status . Effect sizes are defined as the mean change found in a variable divided by the st and ard deviation of that variable . Effect sizes are used to translate “ the before and after changes ” in a “ one group ” situation into a st and ard unit of measurement that will provide a clearer underst and ing of health status results . The utility of effect sizes is demonstrated from four different perspectives using three health status data sets derived from arthritis population s administered the Arthritis Impact Measurement Scales ( AIMS ) . The first perspective shows how general and instrument-specific benchmarks can be developed and how they can be used to translate the meaning of clinical change . The second perspective shows how effect sizes can be used to compare traditional clinical measures with health status measures in a st and ard clinical drug trial . The third application demonstrates the use of effect sizes when comparing two drugs tested in separate drug trials and shows how they can facilitate this type of comparison . Finally , our health status results show how effect sizes can supplement st and ard statistical testing to give a more complete and clinical ly relevant picture of health status change . We conclude that effect sizes are an important tool that will facilitate the use and interpretation of health status measures in clinical research in arthritis and other chronic diseases PURPOSE Although it is the most powerful predictor of early prostate cancer treatment-related complications and quality -of-life ( QOL ) outcomes , most studies do not stratify results by baseline function . Further , reporting functional outcomes as averaged numerical results may obscure informatively disparate courses . Using levels of treatment-related dysfunction , we address these problems and present the final QOL outcomes of our prospect i ve cohort study of patients with early prostate cancer . METHODS We created categories for sexual , bowel , and urinary function , measured using numerical scores of the vali date d Prostate Cancer Symptom Indices and stratified into " normal , " " intermediate " and " poor " levels of function by incorporating patient-reported symptom and distress information . We present QOL outcomes for 409 patients 36 months after radical prostatectomy , external-beam radiation therapy , and brachytherapy .  RESULTS Different levels of baseline sexual , bowel , and urinary function produced distinctive treatment-related changes from baseline to 36 months . In general , the average scale increases in dysfunction were greatest among patients with normal baseline function , although patients with normal and intermediate baseline function had similar increases in sexual dysfunction . For patients whose baseline urinary obstruction/irritation was poor , both average scale scores and most patients ' level of function improved after treatment , particularly after surgery . CONCLUSION The use of functional levels to stratify treatment-related outcomes by pretreatment functional status and to display the proportions of patients with improved , stable , or worsened function after treatment provides information that more specifically conveys the expected impact of treatment to patients choosing among localized prostate cancer treatments To assess the impact of disease and treatment on patients with advanced non-small cell lung cancer ( NSCLC ) , we set out to determine a clinical ly meaningful change ( CMC ) on the Lung Cancer Subscale ( LCS ) and the Trial Outcome Index ( TOI ) of the Functional Assessment of Cancer Therapy-Lung ( FACT-L ) question naire . We used data from Eastern Cooperative Oncology Group study 5592 ( E5592 ) , a r and omized trial comparing three chemotherapeutic regimens in 599 advanced NSCLC patients . Patients completed the FACT-L at baseline ( pretreatment ) , 6 weeks , 12 weeks , and 6 months . Comparing across baseline performance status ( 0 vs. 1 ) , prior weight loss ( < 5 % vs. > or = 5 % ) , and primary disease symptoms ( < or = 1 vs. > 1 ) , LCS and TOI score differences ranged from 2.4 to 3.6 and 6.5 to 9.2 , respectively ( all Ps < .001 ) . Mean improvement in LCS score from baseline to 12 weeks was 2.4 points in patients who had responded to treatment versus 0.0 points in patients who had progressive disease . Twelve-week LCS change scores for patients progressing early were 3.1 points worse than those of patients progressing later ( mean = -1.2 vs.1.9 , respectively ) . Similarly , the average TOI change score from baseline to 12 weeks was -6.1 for patients who had progressive disease versus -0.8 points for patients who had responded to treatment . Twelve-week TOI change scores for patients progressing early ( mean = -8.1 ) were 5.7 points worse than those of patients progressing later ( mean = -8.1 vs. -2.4 , respectively ) . Analyses assuming nonr and om missing data result ed in slightly larger differences . Clinical ly relevant change scores were estimated as two to three points for the LCS and five to seven points for the TOI , setting upper limits for minimal CMCs . These values were comparable to suggested distribution-based criteria of a minimally important difference . These results support use of a two to three point change in the LCS and five to six point change on the TOI of the FACT-L as a CMC , and offer practical direction for inclusion of important patient-based endpoints in lung cancer clinical trials Background . It is generally agreed that r and omized controlled trials should be powered to detect small but clinical ly significant treatment effects . Toward these ends , minimal important difference ( MID ) was proposed as a benchmark for design ing trials and for interpreting health-related quality -of-life instrument scores . MID was defined in 1989 as “ the smallest difference in score in the domain of interest which patients perceive as beneficial and which would m and ate , in the absence of troubling side effects and excessive cost , a change in the patient ’s management . ” Objective . 1 ) To exp and the idea of minimal clinical ly important difference so as to take into account harms as well as benefits . 2 ) To propose concepts and methods with which to do so . Summary . The authors define sufficiently important difference ( SID ) as the smallest amount of patient-valued benefit that an intervention would require to justify associated costs , risks , and other harms . As a means toward estimation of SID , the authors propose benefit-harm tradeoff methods , in which domains of benefit and harm are systematic ally traded off against each other and assessed in relation to the global decision of whether a treatment choice is worthwhile . Specific SID estimates can be used to power and interpret clinical trials or to inform health services research and /or public health policy . This article briefly describes the evolution of the important difference concept and outlines similarities and differences between MID and SID
target: We observed a lack of consistency in the application of methods for computing MID or MCID for generic and disease-specific HRQoL measures .
  The anchor-based method yields conservative estimates of MID or MCID , compared to the distribution-based method .
background: OBJECTIVES The objective of the study was to assess the usability of minimal important difference ( MID ) and minimal clinical ly important difference ( MCID ) for measuring meaningful changes in disease-specific and generic health-related quality -of-life ( HRQoL ) outcomes in patient-centered care .