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|---|---|---|---|---|---|---|---|
Collection Type
|
--DTC --STDTC --ENDTC Single-Point Collection
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
4.1.4.9
|
USE OF DATES AS RESULT VARIABLES Dates are generally used only as timing variables to describe the timing of an event, intervention, or collection activity, but there may be occasions when it may be preferable to model a date as a result (--ORRES) in a Findings dataset. Note that using a date as a result to a Findings question is unusual and atypical, and should be approached with caution, but this situation may occasionally occur when a) a group of questions (each of which has a date response) is asked and analyzed together; or b) the event(s) and intervention(s) in question are not medically significant (often the case when included in questionnaires). Consider the following cases: •
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Calculated due date
|
• Date of last day on the job • Date of high school graduation CDISC SDTM Implementation Guide (Version 3.1.2) One approach to modeling these data would be to place the text of the question in --TEST and the response to the question, a date represented in ISO 8601 format, in --ORRES and --STRESC as long as these date results do not contain the dates of medically significant events or interventions. Again, use extreme caution when storing dates as the results of findings. Remember, in most cases, these dates should be timing variables associated with a record in an Intervention or Events dataset. 4.1.4.10 REPRESENTING TIME POINTS Time points can be represented using the time point variables, --TPT, --TPTNUM, --ELTM, and the time point anchors, --TPTREF (text description) and --RFTDTC (the date/time). Note that time-point data will usually have an associated --DTC value. The interrelationship of these variables is shown in Figure 4.1.1.10 below. Figure 4.1.1.10 Values for these variables for Vital Signs measurements taken at 30, 60, and 90 minutes after dosing would look like the following.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
DOSE ADMINISTRATION
|
2006-08-01T08:00 2006-08-01T09:32 Note that the actual elapsed time is not an SDTM variable, but can be derived by an algorithm representing VSDTC-VSRFTDTC. When time points are used, --TPTNUM is required. Time points may or may not have an associated --TPTREF. Sometimes, --TPTNUM may be used as a key for multiple values collected for the same test within a visit; as such, there is no dependence upon an anchor such as --TPTREF, but there will be a dependency upon the VISITNUM. In such cases, VISITNUM will be required to confer uniqueness to values of --TPTNUM. If the protocol describes the scheduling of a dose using a reference intervention or assessment, then --TPTREF should be populated, even if it does not contribute to uniqueness. The fact that time points are related to a reference time point, and what that reference time point is, are important for interpreting the data collected at the time point. CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
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glossary.json
| null | null |
Page 50
|
CDISC, © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
PRE-DOSE
|
1 1H 2 PERIOD 2, DAY 1 5
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
PERIOD 1
|
3 DAY 5, PM DOSE 4H 3
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
PERIOD 2
|
4 DAY 1, PM DOSE 4H 3 CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 52
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CDISC, © 2007. All rights reserved July 25, 2007
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[
"SDTM.pdf"
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SDTM.pdf
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pdf
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glossary.json
| null | null |
Page 54
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CDISC, © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Chemistry
|
229 mg/dL 0 199 229 229 11
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
BLQ
|
mg/L 0 0 mg/L 7
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Hematology
|
5.9 10^9/L 4 11 5.9 5.9
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Not Done
|
--STAT Populated using a code value in the list of controlled terms, codelist ND (C66789) at http://www.cdisc.org/standards/terminology/index.html
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
QS2
|
HEALTH PERCEPTIONS (0-100) 82 82
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
QSP11
|
EXPECT HEALTH TO GET BETTER
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 56
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CDISC, © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CRF
|
Case report form (sometimes case record form)
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
COMMITTEE
|
4.1.5.5 CLINICAL SIGNIFICANCE FOR FINDINGS OBSERVATION CLASS DATA SDTM provides two ways to handle assessments of clinical significance. Each has its place; they are not interchangeable. One is used to handle assessments of the clinical significance of a particular result (single record), CDISC SDTM Implementation Guide (Version 3.1.2)
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[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 58
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CDISC, © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
LBSEQ
|
48 1 Additional examples may be found in the domain examples such as the examples for Disposition/Adverse Event found in Section 6.2.2.2, Example 4, and all of the Pharmocokinetics examples in Section 6.3.10.5. CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Value of
|
--STAT Spontaneously reported event occurred Pre-specified event occurred
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
DM
|
--REAS Reason (include domain prefix) All general observation classes
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Exp
|
* Indicates variable may be subject to controlled terminology A record in a SUPP-- dataset relates back to its parent record(s) via the key identified by the STUDYID, RDOMAIN, USUBJID and IDVAR/IDVARVAL variables. An exception is SUPP-- dataset records that are related to Demography (DM) records , such as the Intent To Treat (ITT) and Safety (SAFETY) subject-level population flags, where both IDVAR and IDVARVAL will be null because the key variables STUDYID, RDOMAIN, and USUBJID are sufficient to identify the unique parent record in DM (DM has one record per USUBJID). All records in the SUPP-- datasets must have a value for QVAL. Transposing source variables with missing/null values may generate SUPP-- records with null values for QVAL, causing the SUPP-- datasets to be extremely large. When this happens, the sponsor must delete the records where QVAL is null prior to submission. See Section 4.1.5.3 for information on representing information greater than 200 characters in length. CDISC SDTM Implementation Guide (Version 3.1.2) See Appendix C5 for controlled terminology for QNAM and QLABEL for some of the most common Supplemental Qualifiers. Additional QNAM values may be created as needed, following the guidelines provided in the CDISC Notes for QVAL. 8.4.2 SUBMITTING SUPPLEMENTAL QUALIFIERS IN SEPARATE DATASETS Beginning with the SDTMIG V3.1.1, the preferred approach is to submit Supplemental Qualifiers by domain rather than placing all of the supplemental information within one dataset. Therefore, it is recommended that sponsors who utilize the single SUPPQUAL approach begin to transition to individual SUPP-- datasets by domain. The single SUPPQUAL dataset option will be deprecated in the next (post V3.1.2) release. Following this convention for the Supplemental Qualifiers produces a one-to-one correspondence between a domain dataset and its Supplemental Qualifier dataset. In such cases, the set of Supplemental Qualifiers for each domain should be included in a separate dataset with the name SUPP-- where -- denotes the source domain which the Supplemental Qualifiers relate back to. For example, population flags and other demographic qualifiers would be placed in suppdm.xpt. Data may have been additionally split into multiple datasets (see Section 4.1.1.6.1, Splitting Domains). Sponsors must, however, choose only one approach for each study. Either individual SUPP-- datasets for each domain where needed should be submitted, or a single SUPPQUAL dataset for the entire study. In other words, separate SUPP-- datasets cannot be used with some domains and SUPPQUAL for the others. 8.4.3 SUPP-- EXAMPLES The examples below demonstrate how a set of SUPP-- datasets could be used to relate non-standard information to a parent domain.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Perm
|
* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI code-list code value) 7.5.1 ASSUMPTIONS FOR TI DATASET 1. If inclusion/exclusion criteria were amended during the trial, then each complete set of criteria must be included in the TI domain. TIVERS is used to distinguish between the versions. 2. Protocol version numbers should be used to identify criteria versions, though there may be more versions of the protocol than versions of the inclusion/exclusion criteria. For example, a protocol might have versions 1, 2, 3, and 4, but if the inclusion/exclusion criteria in version 1 were unchanged through versions 2 and 3, and only changed in version 4, then there would be two sets of inclusion/exclusion criteria in TI, one for version 1, one for version 4. 3. Individual criteria do not have versions. If a criterion changes, it should be treated as a new criterion, with a new value for IETESTCD. If criteria have been numbered and values of IETESTCD are generally of the form INCL00n or EXCL00n, and new versions of a criterion have not been given new numbers, separate values of IETESTCD might be created by appending letters, e.g. INCL003A, INCL003B. 4. IETEST contains the text of the inclusion/exclusion criterion. However, since entry criteria are rules, the variable TIRL has been included in anticipation of the development of computer executable rules. 5. Assumption 5 for the IE Domain (Section 6.3.2.1) describes how to handle values of IETEST > 200 characters. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 60
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Timing
|
1. Planned study day of VISIT. 2. Due to its sequential nature, used for sorting.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
DMDY
|
Study Day of Collection Num
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 62
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
ABC12301001
|
001 2006-01-12 2006-03-10 01 JOHNSON, M 1948-12-13 57
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
ABC12301002
|
002 2006-01-15 2006-02-28 01 JOHNSON, M 1955-03-22 50
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
ABC12301003
|
003 2006-01-16 2006-03-19 01 JOHNSON, M 1938-01-19 68
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
ABC12301004
|
004 01 JOHNSON, M 1941-07-02 5
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
ABC12302001
|
001 2006-02-02 2006-03-31 02 GONZALEZ, E 1950-06-23 55
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
ABC12302002
|
002 2006-02-03 2006-04-05 02 GONZALEZ, E 1956-05-05 49
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
USA
|
CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Check One
|
American Indian or Alaska Native
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Asian
|
Black or African American Native Hawaiian or Other Pacific
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
HISPANIC OR LATINO
|
CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Check all that apply
|
American Indian or Alaska Native
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
White
|
Other, Specify: _____________________ Row 1 (DM), Row 1 (SUPPDM) Subject 001 checked “Other, Specify” and entered “Japanese” which was mapped to “Asian” by the sponsor. Row 2 (DM), Row 2 (SUPPDM) Subject 002 checked “Other, Specify” and entered “Swedish” which was mapped to “White” by the sponsor.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Race 2
|
AMERICAN INDIAN OR ALASKA NATIVE
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 64
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Non-Japanese
|
Black or African American Native Hawaiian or Other Pacific
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 66
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Run-In
|
2006-05-21 2006-05-26 CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 68
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CO
|
Identifier Two-character abbreviation for the domain.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 70
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
INVESTIGATOR
|
Note that the use of 10^9 as a unit is not a standard representation. CDISC, © 2007. All rights reserved
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 72
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
SE
|
Identifier Two-character abbreviation for the domain.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Req
|
• * indicates variable may be subject to controlled terminology CDISC SDTM Implementation Guide (Version 3.1.2) 8.2.2 RELREC DATASET EXAMPLES Example 1: This example shows how to use the RELREC dataset to relate records stored in separate domains for USUBJID 123456 who had two lab tests performed (Rows 5 and 6) and took two concomitant medications (Rows 2 and 3) as the result of an adverse event (Rows 1 and 4). This example represents a situation in which the adverse event is related to both the concomitant medications and the lab tests, but there is no relationship between the lab values and the concomitant medications
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Example 1
|
In the two rows of suppdm.xpt, population flags are defined as supplemental information to a subject’s demographic data. IDVAR and IDVARVAL are null because the key variables STUDYID, RDOMAIN, and USUBJID are sufficient to identify a unique parent record in DM. suppdm.xpt: Supplemental Qualifiers for DM Row STUDYID RDOMAIN USUBJID IDVAR IDVARVAL QNAM
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
COUNTRY
|
Populated using a code value in the list of controlled terms, codelist COUNTRY (C66786) at http://www.cdisc.org/standards/terminology/index.html
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 74
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CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Example 2
|
The two rows of suppae.xpt add qualifying information to adverse event data (RDOMAIN=AE). IDVAR defines the key variable used to link this information to the AE data (AESEQ). IDVARVAL specifies the value of the key variable within the parent AE record that the SUPPAE record applies to. The remaining columns specify the supplemental variables’ names (AESOTHC and AETRTEM), labels, values, , and who made the evaluation. suppae.xpt: Supplemental Qualifiers for AE Row STUDYID RDOMAIN USUBJID IDVAR IDVARVAL
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
DBA
|
2006-05-03 2006-05-31 2 DOUBLE-BLIND TREATMENT 5 456 3
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
UNPLAN
|
2006-05-31 2006-06-13 Drug B dispensed in error DOUBLE-BLIND TREATMENT 6 456 4
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 76
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
SV
|
101 4.1 2006-02-07 2006-02-07 18 18
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
SVSTDTC
|
Start Date/Time of Visit Char
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
SVENDTC
|
End Date/Time of Visit Char
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Screen
|
Example Trial 6: Arms and Epochs Screen Trt A R A Trt A R A Trt A R A Trt A R A Follow
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
DAY 1
|
1 2001-02-01T18:30 1 600 min 12
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Week 1
|
1999-06-19 8 (cont) 5.00 9.00
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Week 2
|
1999-07-21 11 (cont) 10^3/uL 4 11
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Follow-up
|
Decision not to treat further 4 weeks
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 78
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Observation Classes
|
6.1 INTERVENTIONS 6.1.1 CONCOMITANT MEDICATIONS — CM cm.xpt, Concomitant Medications — Interventions, Version 3.1.2, July 25, 2007. One record per recorded intervention occurrence or constant-dosing interval per subject, Tabulation
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Char CM
|
Identifier Two-character abbreviation for the domain.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CMSPID
|
Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Examples: a number pre-printed on the CRF as an explicit line identifier or record identifier defined in the sponsor’s operational database. Example: line number on a concomitant medication page.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CMTRT
|
Reported Name of Drug, Med, or Therapy
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 80
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CMCAT
|
Category for Medication Char *
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CMROUTE
|
Route of Administration Char (ROUTE)
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 82
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
References
|
MBORRESU Original Units Char *
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Time Point
|
Char BEFORE, AFTER, COINCIDENT, ONGOING, U
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CMSTTPT
|
Start Reference Time Point Char
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
CMENTPT
|
End Reference Time Point Char
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
Page 84
|
CDISC. © 2007. All rights reserved July 25, 2007
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
VPA
|
Example 3: Pre-specified concomitant medications using CMPRESP, CMOCCUR, CMSTAT, and CMREASND Sponsors often are interested in whether subjects are exposed to specific concomitant medications, and collect this information using a checklist. The example below is for a study that has a particular interest in the antidepressant medications that subjects use. For the study’s purposes, the absence is just as important as the presence of a medication. This can be clearly shown by using CMOCCUR. In this example, CMPRESP shows that the subjects were specifically asked if they use any of three antidepressants (Zoloft, Prozac, or Paxil). The value of CMOCCUR indicates the response to the pre-specified medication question. CMSTAT indicates whether the response was missing for a pre-specified medication and CMREASND shows the reason for missing response. The medication details (e.g., dose, frequency) are not of interest.
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Char EX
|
Identifier Two-character abbreviation for the domain.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
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glossary.json
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EXSPID
|
Sponsor-Defined Identifier Char Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an explicit line identifier or defined in the sponsor’s operational database. Example: Line number on a CRF Page.
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"SDTM.pdf"
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SDTM.pdf
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EXTRT
|
Name of Actual Treatment Char
|
[
"SDTM.pdf"
] |
SDTM.pdf
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pdf
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glossary.json
| null | null |
EXCAT
|
Category for Treatment Char *
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
EXSCAT
|
Subcategory for Treatment Char *
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
EXROUTE
|
Route of Administration Char (ROUTE)
|
[
"SDTM.pdf"
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SDTM.pdf
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pdf
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| null | null |
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EXTPT
|
Planned Time Point Name Char
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
EX Definition
|
a. The Exposure domain model records the details of a subject’s exposure to protocol-specified study treatment. Study treatment may be any intervention that is prospectively defined as a test material within a study, and is typically but not always supplied to the subject. Examples include but are not limited to placebo, active comparators, and investigational products. Treatments that are not protocol-specified should be recorded in the Concomitant Medication (CM) domain. b. This domain should contain one record per constant dosing interval per subject. "Constant dosing interval" is sponsor-defined, and may include any period of time that can be described in terms of a known treatment given at a consistent dose. E.g., for a study with once-a-week administration of a standard dose for 6 weeks, exposure may be represented with a single record per subject, spanning the entire treatment phase. Or if the sponsor monitors each treatment administration and deviations in treatment or dose occur, there could be up to six records (one for each weekly administration). 2. Categorization and Grouping a. EXCAT and EXSCAT may be used when appropriate to categorize treatments into categories and subcategories. For example, if a study contains several active comparator medications, EXCAT may be set to 'ACTIVE COMPARATOR.' Such categorization will not be useful in most studies, so these variables are permissible but not expected. 3. Exposure Treatment Description a. EXTRT captures the name of the investigational treatment and it is the topic variable. It is a required variable and must have a value. EXTRT should only include the treatment name and should not include dosage, formulation or other qualifying information. For example, “ASPIRIN 100MG TABLET” is not a valid value for EXTRT. This example should be expressed as EXTRT= “ASPIRIN”, EXDOSE= “100”, EXDOSU= “MG”, and EXDOSFRM= “TABLET”. CDISC SDTM Implementation Guide (Version 3.1.2) CDISC, © 2007. All rights reserved
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"SDTM.pdf"
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Timing Variables
|
a. Relative timing assessments "Prior" or “Ongoing” are common in the collection of Clinical Event information. CESTRF or CEENRF may be used when this timing assessment is relative to the study reference period for the subject represented in the Demographics dataset (RFENDTC). CESTRTPT with CESTTPT or CEENRTPT with CEENTPT may be used when "Prior" or "Ongoing" are relative to specific dates other than the start and end of the study reference period. See Section 4.1.4.7. b. Additional Timing variables may be used when appropriate. 5. Additional Events Qualifiers The following qualifiers would generally not be used in CE: --SER, --ACN, --ACNOTH, --REL, --RELNST, --OUT, --SCAN, --SCONG, --SDISAB, --SDTH, --SHOSP, --SLIFE, --SOD, --SMIE CDISC, © 2007. All rights reserved
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EXDOSFRM
|
EXDOSFRQ EXDOSTOT EXROUTE EXSTDTC EXENDTC EXSTDY EXENDY 1 12345
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[
"SDTM.pdf"
] |
SDTM.pdf
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pdf
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glossary.json
| null | null |
ORAL
|
2004-07-10T07:30 2004-07-10T07:30 10 10 12 (cont) 800
|
[
"SDTM.pdf"
] |
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Page 90
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TABLET
|
2002-07-02 2002-08-01 Increased due to suboptimal efficacy CDISC SDTM Implementation Guide (Version 3.1.2) Exposure Example 4: This is an example of a titration Exposure dataset for a study that gradually increases dosage while simultaneously evaluating efficacy and toleration of the treatment regimen. The schedule specifies that Drug A be administered twice daily starting with 100 mg for 3 days, then increase to 200 mg daily for 3 days, then increase further in 100-mg increments every three days until signs of intolerance are noted or no improvement in efficacy is observed. Row STUDYID DOMAIN USUBJID EXSEQ EXGRPID EXTRT EXDOSE EXDOSU
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Char SU
|
Identifier Two-character abbreviation for the domain.
|
[
"SDTM.pdf"
] |
SDTM.pdf
|
pdf
|
glossary.json
| null | null |
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