- Created by Christine Connolly, last modified on Mar 19, 2023
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Guidance in this section describes the intended population of tabulation dataset records and variables and will be used with domain-specific assumptions as appropriate.
The following are general conventions for variable population:
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2 | Text Strings Greater Than 200 Characters | When text strings greater than 200 characters are collected, the following conventions for general observation class variables and SUPP-- datasets will be adhered to:
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3 | "Yes", "No", Values |
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4 | --SEQ, --RECID | Variables --SEQ and --RECID are populated to explicitly identify domain records in different ways. Differences in variable population are described below.
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5 | --GRPID |
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6 | --REFID |
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7 | --CAT, --SCAT |
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8 | --STAT |
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The following are conventions for variable population in Interventions and Events class domains.
Num | Variable Population | Implementation | ||||||||||||||||||||||||||||||
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Multiple Values for a Topic Variable | Product Impact on Individual Health Only: Interventions and events class datasets will conform with the record structure defined for the domain. If multiple values are reported for an intervention or event topic variable (i.e., for variables --TRT or --TERM), the applicant will split the values into multiple records or otherwise resolve the multiplicity per the applicant's data management procedures. For example, if an adverse event term of "Headache and nausea" is reported, sponsors will often split the original report into separate records and/or query the site for clarification. | |||||||||||||||||||||||||||||||
1 | Prespecified interventions and events (--PRESP, --OCCUR, --STAT, REASND) | Product Impact on Individual Health Only: Interventions (e.g., concomitant medications) and events (e.g., medical history) can be collected as responses to a prespecified list of treatments or terms. In such cases:
The following table shows the population of --PRESP, --OCCUR, --STAT, and --REASND for different data collection scenarios.
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The following are conventions for variable population in Findings class domains.
Num | Variable Population | Implementation | |||||||||||||||||||||
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1 | Result Precision |
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2 | Standardized Units | Applicants may standardize units within a study for a given test per scientific and regulatory requirements. Standardization of units is recommended when data for the same test are collected via different sources using different units. In such cases, --ORRESU will represent the collected unit and --STRESU will represent the standardized unit. | |||||||||||||||||||||
3 | Original and Standardized Results (--ORRES, --ORRESU, --STRESC, --STRESU, --STRESN) | The following is applicable to results that are not collected via QRS instruments. Please refer to QS domain assumptions for expected variable population.
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4 | Tests Not Done | When an entire examination (e.g., Laboratory Test Results (LB)), a group of tests (e.g., hematology or urinalysis), or an individual test (e.g., glucose) is not done for a USUBJID, POOLID, or SPTOBID and this information is explicitly captured with or without the reason for not collecting the information, record(s) can be created in the dataset to represent these data. In such cases, applicants may include:
For example, if a group of hematology or urinalysis tests represented in the LB domain are not done for a subject, then:
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5 | Biological Significance | Nonclinical Only:
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6 | Clinical Significance | Product Impact on Individual Health Only:
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7 | Derived Records |
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