anchor.diff:  
       <h2>How can I objectively identify important differences?</h2>
   
  -    <p>In practice, p-values and confidence intervals are often employed even when there is no random data generating process in order to help highlight differences that are larger than “noise” (e.g., authors may want to highlight differences with a p-value < .001). While this practice may not have a strong foundation in statistical philosophy, we acknowledge that many audiences (e.g., journal peer reviewers) may demand p-values because they appear to provide objective criteria for identifying larger-than-expected signals in the data. While we don’t encourage reporting of p-values for this purpose, we do encourage analysts to specify objective criteria for evaluating signals in the data. For example,</p>
  +    <p>In practice, p-values and confidence intervals are often employed even when there is no random data generating process to highlight differences that are larger than “noise” (e.g., authors may want to highlight differences with a p-value < .001).  While this practice may not have a strong foundation in statistical philosophy, we acknowledge that many audiences (e.g., journal peer reviewers) may demand p-values because they appear to provide objective criteria for identifying larger-than-expected signals in the data.  While we don’t encourage reporting of p-values for this purpose, we do encourage analysts to specify objective criteria for evaluating signals in the data.  Examples are provided:</p>
   
       <p>a) Prior to examining the data, specify comparisons of major interest, or quantities to be estimated. </p>
   
  -    <p>b) Determine the magnitude of differences that would have practical significance. E.g., a 25% difference in source of funding between studies of two pediatric conditions, or a difference in enrollment of 100 participants.</p>
  +    <p>b) Determine the magnitude of differences that would have practical significance.  (e.g., a 25% difference in source of funding between studies of 2 pediatric conditions, or a difference in enrollment of 100 participants).  </p>
   
  -    <p>c) Determine appropriate formulas for quantifying differences between groups or summarizing population variability. This quantification could take account of the observed difference, variability in the data, and the number of observations. For example,</p>
  +    <p>c) Determine appropriate formulas for quantifying differences between groups or summarizing population variability.  This quantification could take into account of the observed difference, variability in the data, and the number of observations.  Examples are provided:</p>
   
       <ul>
         <li>When summarizing a continuous characteristic such as enrollment, the analyst might choose to report the median and 5th to 95th percentiles.</li>
  -      <li>To quantify signal to noise, the analyst could calculate a t-statistic or a chi-squared statistic (without the p-value) and rank differences between two groups based on these values. The analyst might pre-specify a threshold (e.g., absolute value of 3) to flag notable differences.</li>
  +      <li>To quantify signal to noise, the analyst could calculate a t-statistic or a chi-squared statistic (without the p-value) and rank differences between 2 groups based on these values.  The analyst might pre-specify a threshold (e.g., absolute value of 3) to flag notable differences.</li>
       </ul>
   
  -    <h2>Other tips</h2>
  +    <h2>Specific tips for working with the AACT database</h2>
   
       <ul>
  -      <li>Users are encouraged to refer to the Schema diagram in the Comprehensive Data Dictionary to determine the relationships between the different data sets that comprise AACT. These relationships determine how data sets may be merged together.</li>
  +      <li>Users are encouraged to use the Schema Diagram xxxxx to determine relationships between different AACT tables/datasets (referred to as ‘datasets’ on this page). These relationships determine how datasets may be linked or merged together in software such as SAS & SQL.</li>
   
  -      <li>Each table contains the NCT_ID which uniquely identifies the study related to the data. Every row in every study-related table is linked to its study through this NCT_ID.</li>
  +      <li>The NCT_ID uniquely identifies each study; it serves as the primary key in the STUDIES dataset.  In other words, each record in the STUDIES dataset has a unique NCT_ID value.  NCT_ID also appears in every dataset related to STUDIES so that every record in every dataset can link back to the study to which it refers.</li>
   
  -      <li>The MESH_THESAURUS table does not have a formal relationship with AACT tables. The MESH_THESAURUS is provided so that users may look up a particular MeSH term (MESH_TERM) in the thesaurus, for example, to determine the position of this term in the MeSH trees hierarchy (MESH_ID). Information in the MESH_THESAURUS table is periodically updated from NLM’s MeSH Site. For more information on MeSH, please refer to the section below titled ‘MeSH terms in CONDITION_BROWSE and INTERVENTION_BROWSE’ and to NLM’s MeSH Site.</li>
  +      <li>Every dataset other than STUDIES has a primary key named ‘id’, which provides an integer that uniquely identifies each row in that dataset. (The STUDIES dataset uses ‘NCT_ID’ instead of ‘id’  as the unique identifier for each row.)</li>
  +			<li>To link dataset information to the study to which it refers, you simply match the NCT_IDs.  For example, every record in the CONDITIONS dataset with an NCT_ID of ‘NCT0000001’ refers to the study with the NCT_ID: ‘NCT0000001’ (saved in STUDIES.nct_id).  The CONDITIONS dataset may contain multiple records with an NCT_ID of  ‘NCT0000001’ which means this study was defined in ClinicalTrials.gov as being associated with the conditions listed in CONDITIONS with that NCT_ID.</li>
  +			<li>Information in several datasets are also related to information in other datasets.  In this case, the dataset that belongs to another dataset will include a foreign key that identifies the record to which it belongs. Foreign keys are always named according to a simple rule:  the singular name of the related dataset followed by: ‘_id’.   For example, FACILITY_CONTACTS includes a data element: facility_id which is the foreign key to FACILITY.id</li>
  +			<li> Each record’s foreign_key (ie. facility_id) contains the value of the unique identifier (id) of the record in the other dataset to which it belongs. For example, a facility may have multiple contacts.  To find the contacts for a particular facility, look for the records in FACILITY_CONTACTS where the value in facility_id is same as the value in id for that facility in FACILITIES.  In short, datasets are related to each other with this pattern: dataset1.<dataset2_name>_id = dataset2.id </li>
  +
       </ul>
  +		<p>Example:</p>
  +		<p>facilities.id = contacts.facility_id  (this finds  all contacts for a facility)</p>
  +		<p>or inversely</p>
  +		<p>contacts.facility_id = facilities.id   (this finds the facility for a particular contact)</p>
   
  -    <h2>Use of the GROUPS table</h2>
  +		<h2>Information about trial sites (FACILITIES and COUNTRIES) </h2>
  +		<p> Information about organizations (trial sites) where the study is/was conducted is stored in the FACILITIES dataset.  This is a snapshot of the facility information that was included in the study record on the date that information was downloaded from ClinicalTrials.gov.  Naturally, study sites initiated after ClinicalTrials.gov data were downloaded to AACT are not included in AACT.</p>
  +		<p>AACT also includes a COUNTRIES dataset, which contains one record per unique country per study. Rows in the COUNTRIES dataset come from two sources:</p>
  +		<ul>
  +			<li>Countries where study facilities are located as determined by an NLM algorithm that identifies the set of unique countries associated with a study; these are stored in the COUNTRIES dataset with the flag removed=false. </li>
  +			<li>In some cases, ClinicalTrials.gov data submitters remove sites from the study record.  Removed sites do not appear in the FACILITIES dataset. If all of a country’s sites have been removed from a study record, the NLM algorithm creates a ‘Removed Country’ record. In AACT, this record is stored in the COUNTRIES dataset with removed=true. </li>
   
  -    <p>The GROUPS table contains records that describe the arms or groups in the clinical study. For example a two arm study may assign participants to one of two study arms: the first in which participants receive placebo drug in addition to standard of care, and the second in which participants receive an experimental drug in addition to standard of care. Information about arms or groups is provided by the data submitter when a study is first registered with ClinicalTrials.gov. Once the study is completed, results data may be submitted to ClinicalTrials.gov. At that time results will be described for each arm or group (and potentially also for the overall study population), along with a description of the arm or group.</p>
  +			<p>The reasons sites are removed from the FACILITIES dataset are varied but unknown.  A site may have been removed because it was never initiated or because it was entered with incorrect information.  The recommended action for sites that have completed or have terminated enrollment is to change the enrollment status to “Completed” or “Terminated”; however, such sites are sometimes deleted from the study record by the responsible party.  Data analysts may consider using Countries where  removed=TRUE to supplement the information about trial locations that is contained in FACILITIES, particularly for studies that have completed enrollment and have no records in FACILITIES.</p>
   
  -    <p>Users may wish to merge records from the GROUPS table with records in one or more of the results data sets, for example to see the age distribution of participants in the Placebo arm. This can be achieved by joining the required tables (e.g., baseline_measures with groups) by the NCT_ID & ctgov_group_enumerator.</p>
  +			<p>Users who are interested in identifying countries where participants are being/were enrolled may use either the FACILITIES or COUNTRIES dataset (where COUNTRIES.removed=false) with equivalent results.</p>
   
  -    <p>Information about trial sites (FACILITIES, REMOVED_COUNTRIES, and LOCATION_COUNTRIES)
  -    In some cases, data submitters remove sites from the study record. If a site was removed before the data were downloaded then it is not included in the FACILITIES dataset. If all sites in a country have been removed from the study record for a particular NCT_ID then an algorithm run at the National Library of Medicine (NLM) creates a record for that country and NCT_ID in the REMOVED_COUNTRIES table. The reasons why sites are removed from the FACILITIES table are varied but unknown. For example, a site may have been removed because it was never initiated or because it was entered with incorrect information. The recommended action for sites that have completed or terminated enrollment is to change the enrollment status to “Completed” or “Terminated”, however, such sites are sometimes deleted from the study record by the responsible party. Data analysts may consider using information from the REMOVED_COUNTRIES dataset to supplement the information about trial locations that is contained in FACILITIES, particularly for studies that have completed enrollment and have no records in FACILITIES.</p>
   
  -    <p>The Countries table provides unique instances of Countries identified for a study in Facilities The COUNTRIES table is created by a National Library of Medicine (NLM) algorithm from the FACILITIES table. This algorithm identifies the unique facility countries associated with each NCT_ID in the FACILITIES table and stores these countries in the COUNTRIES table for each NCT_ID. In general, all countries listed in the FACILITIES table for a particular NCT_ID were also listed in the COUNTRIES table for that NCT_ID, and vice versa. Users who are interested in identifying countries where participants are being/were enrolled may use either the FACILITIES or the COUNTRIES tables with equivalent results.</p>
  +		<h2>MeSH terms in BROWSE_CONDITIONS and BROWSE_INTERVENTIONS</h2>
  +    <p>When data submitters provide information to ClinicalTrials.gov about a study, they’re encouraged to use Medical Subject Heading (MeSH) terminology for interventions, conditions, and keywords.  The BROWSE_CONDITIONS and BROWSE_INTERVENTIONS datasets contain MeSH terms generated by an algorithm run by NLM. The NLM algorithm is re-run nightly on all studies in the ClinicalTrials.gov database, and sources the most up-to-date information in the study record, the latest version of the algorithm, and the version of the MeSH thesaurus in use at that time.</p>
   
  -    <b>MeSH terms in CONDITION_BROWSE and INTERVENTION_BROWSE</b>
  -    <p>When data submitters provide information about studies at ClinicalTrials.gov they are encouraged to use Medical Subject Heading (MeSH) terminology in fields such as interventions, conditions, and keywords. The CONDITION_BROWSE and INTERVENTION_BROWSE tables contain relevant MeSH terms generated by an NLM algorithm that may be used to supplement the information about study conditions and interventions supplied by the data submitter. The CONDITION_BROWSE and INTERVENTION_BROWSE tables contain Medical Subject Heading (MeSH) terms generated by an algorithm run by the National Library of Medicine (NLM). The NLM algorithm is re-run nightly on all studies in the ClinicalTrials.gov database, and sources the most up-to-date information in the study record, the latest version of the algorithm, and the version of the MeSH thesaurus in use at that time.</p>
  -
  -    <b>“Delayed Results” data elements available in AACT</b>
  +		<h2>>“Delayed Results” data elements available in AACT</h2>
   
       <p>A responsible party may delay the deadline for submitting results information to ClinicalTrials.gov if one of the following two certification conditions applies to the trial:</p>
   
  
positive.code:     <h2>References</h2>
  
      <ol>
        <li>Zarin, D. A., Tse, T. T., Williams, R. J., Califf, R. M., and Ide, N. C. (2011). <a href="">The ClinicalTrials.gov results database – update and key issues</a>. N Engl J Med 364: 852–60.</li>
        <li><a href="">Food and Drug Administration Amendments Act of 2007</a>. Public Law 110-95.</li>
        <li>Laine, C., Horton R., DeAngelis C.D., et al. <a href="">Clinical trial registration – looking back and moving ahead</a>. N Engl J Med 356: 2734–6.</li>
        <li><a href="">Communication from the Commission regarding the guideline on the data fields contained in the clinical trials database provided for in Article 11 of Directive 2001/20/EC to be included in the database on medicinal products provided for in Article 57 or Regulation (EC) No 726/2004</a>. In: European Commission, ed. Official Journal of the European Union, 2008. (2008/C 168/02.)</li>
        <li><a href="">Guidance on the information concerning paediatric clinical trials to be entered into the EU Database on Clinical Trials (EudraCT) and on the information to be made public by the European Medicines Agency (EMEA), in accordance with Article 41 of Regulation (EC) No 1901/2006</a>. In: European Commission, ed. Official Journal of the European Union, 2009. (2009/C 28/01.)</li>
        <li>Zarin, D. A., Ide, N. C., Tse, T. et al. (2007). <a href="">Issues in the registration of clinical trials</a>. JAMA 297: 2112—2120.</li>
        <li>Moher, D., Liberati, A., Tetzlaff, J. and Altman, D. G. (for the PRISMA Group)(2009). <a href="">Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement</a>, BMJ 339: 332—336.</li>
        <li>Tse, T., Williams, R. J., Zarin, D. A. (2009). <a href="">Reporting “basic results” in ClinicalTrials.gov</a>. CHEST 136: 295—303.</li>
      </ol>
  
  
    </div>
  </section>
  
negative.code: <section class="pointsToConsider">
    <div class="wrap">
  
      <h2>What is AACT?</h2>
  
      <p>AACT is the database for Aggregate Analysis of ClinicalTrials.gov. Version 2 of AACT is a postgreSQL relational database containing information about clinical studies that have been been registered at ClinicalTrials.gov. AACT includes all of the protocol and results data elements for studies that are publicly available at ClinicalTrials.gov. Content is downloaded daily from ClinicalTrials.gov and loaded into AACT.</p>
  
      <p> <a href="http://ec2-52-87-233-209.compute-1.amazonaws.com/aact_points_to_consider.docx" download>Download a word document</a> containing the   information on this page.</p>
  
      <h2>What population of studies is represented in AACT? </h2>
  
      <p>All studies registered and publicly available at ClinicalTrials.gov are included in AACT. The ClinicalTrials.gov was released for the registration of studies on February 29, 2000. The registry accepts interventional studies in which participants are assigned according to a research protocol to receive specific interventions, as well as observational studies. It also includes Expanded Access records which describe the procedure for obtaining an experimental drug or device for patients who are not adequately treated by existing therapy and who are unable to participate in a controlled clinical study.</p>
  
      <p>The registration of studies and reporting of results and adverse events has been mandated to a large extent by requirements (both legal and institutional) implemented as part of the Food and Drug Administration Amendments Act (FDAAA), as well as by requirements introduced by the International Committee of Medical Journal Editors (ICMJE) and the European Medicines Agency (EMA) regarding registration of clinical studies. Table 1 describes the scope of these requirements.
  Table 1: Scope of Interventional Studies Covered by Major Reporting Policiesxxxxxxx</p>
  
      <p>Based on these requirements, the following are examples of characteristics that may influence the likelihood that a study is included in the ClinicalTrials.gov registry:</p>
  
  