Study Data Tabulation Model (SDTM) Automation with SAS

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clinical data
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Automation of SDTM-Demographics (DM)/SUPP creation in SAS for CDISC-compliant clinical data using a metadata-driven approach.
Author

Ousmane DIALLO

Published

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Automation of SDTM-Demographics (DM) creation in SAS for CDISC-compliant clinical data using a metadata-driven approach.

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Introduction

The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) is the regulatory standard for organizing and formatting clinical trial data for submission to health authorities like the FDA and EMA. This project demonstrates a metadata-driven approach to automate SDTM Demographics (DM) domain creation using SAS.

What is SDTM?

SDTM provides a standardized structure for clinical trial data that:

  • Ensures consistency across studies and sponsors

  • Facilitates regulatory review and approval processes

  • Enables data integration and meta-analyses

  • Reduces submission timelines through standardization

Project Overview

Objective

Build SDTM DM (Demographics) and SUPPDM datasets from raw clinical data using an automated, metadata-driven workflow that ensures CDISC compliance and reproducibility.

Key Benefits

  • Metadata-driven: Single source of truth for variable specifications

  • Reproducible: Standardized workflow across studies

  • Compliant: Ensures CDISC SDTMIG v3.4 standards

  • Efficient: Reduces manual programming errors

Technical Approach

Raw Data → Metadata → Empty Shell → Mapping → Derivations → Validation → Export

Deliverables

  • TARGET.DM: One record per subject with demographics data

  • TARGET.SUPPDM: Supplemental qualifiers for non-standard variables

  • Validation Reports: QC checks ensuring data integrity

    Data Inputs

    Metadata Specification:

    • File: SDTM_METADATA1.csv (variables, roles, lengths, types, sort keys).

      • Variable definitions (names, types, lengths, labels)

      • CDISC role classifications (Identifier, Timing, Qualifier)

      • Sort order specifications

    Source Data:

    • SOURCE.DEMOGRAPHIC (1 row per subject, e.g., subject, dob, gender, trt, uniqueid)

    • SOURCE.DOSING (e.g., subject, startdt, enddt)

    Environment Setup

    • Platform: SAS OnDemand 9.4

    • Libraries: SOURCE (raw data), LIB (formats), TARGET (outputs)

Technical Implementation

1. Library Setup

```{sas}         
/* Common paths & librefs */
%include "/home/u64197545/CDISC/appendix/common.sas";
/* expects:
 libname source "/home/u64197545/CDISC/data";
 libname library "/home/u64197545/CDISC/formats";
 libname target "/home/u64197545/CDISC/sdtm";
*/

2. Create EMPTY DM from Metadata

The make_empty_dataset macro creates dataset templates with proper CDISC attributes:

```{sas}         
/* Create empty based on metadata CSV */
%macro make_empty_dataset(metadatafile=, dataset=);
/* Import csv file and rename it _temp */
    proc import datafile="&metadatafile" out=_temp dbms=csv replace;
        guessingrows=max;
        delimiter=";";
        encoding='utf-8';
    run;

    /* Sort the dataset by expected specified variable order (varnum) */
    proc sort data=_temp;
        where domain="&dataset";
        by varnum;
    run;

    /* Create keepstring macro variable and load metadata information into macro variables */
    %global &dataset.KEEPSTRING;

    data _null_;
        set _temp nobs=nobs end=eof;
        if _n_=1 then
            call symput("vars", compress(put(nobs, 3.)));
        call symputx('var'    || compress(put(_n_, 3.)), variable);
        call symputx('label'  || compress(put(_n_, 3.)), label);
        call symputx('length' || compress(put(_n_, 3.)), put(length, 3.));
        /* Valid ODM types include TEXT, INTEGER, FLOAT, DATETIME, DATE, TIME and map to SAS numeric or character */
        if upcase(type) in ("INTEGER", "FLOAT") then
            call symputx('type' || compress(put(_n_, 3.)), "");
        else if upcase(type) in ("TEXT", "DATE", "DATETIME", "TIME") then
            call symputx('type' || compress(put(_n_, 3.)), "$");
        else
            put "ERR" "OR: not using a valid ODM type.  " type=;
        /* Create KEEPSTRING macro variable */
        length keepstring $ 32767;
        retain keepstring;
        keepstring=compress(keepstring) || "|" || left(variable);
        if eof then
            call symputx(upcase(compress("&dataset" || 'KEEPSTRING')), 
                left(trim(translate(keepstring, " ", "|"))));
    run;

    /* Create a 0-observation template data set used for assigning variable attributes */
    data EMPTY_&dataset;
        %do i=1 %to &vars;
            attrib &&var&i label="&&label&i" 
            %if "&&length&i" ne "" %then
                length=&&type&i.&&length&i...;
            ;
            %if &&type&i=$ %then
                retain &&var&i '';
            %else
                retain &&var&i .;
            ;
        %end;
        if 0;
    run;
%mend make_empty_dataset;

/* Result: WORK.EMPTY_DM with 0 obs, all expected variables/attributes */

3. STDM Variable Mapping

SDTM DM variables are classified according to CDISC standards (SDTMIG v3.4):

Variable Class Examples Purpose
Identifier STUDYID, DOMAIN, USUBJID, SUBJID Unique subject identification
Timing RFSTDTC, RFENDTC, BRTHDTC Study and reference dates (ISO 8601)
Qualifier SEX, RACE, COUNTRY, AGE Demographic characteristics
Treatment ARMCD, ARM, ACTARM Planned and actual treatments

4. Controlled Terminology

```{sas}         
/* Creating custom format for the variables sex, race, arm, and armcd */

proc format;
    value sex 1='M' 2='F' .='U';
    value race 1='White' 2='Black or African American' 3='Asian';
    value armcd 0='PLACEBO' 1='ALG123';
    value arm 0='Placebo' 1='Analgezia HCL 30mg';
run;

5. Reference Date Derivation

Calculate first and last dosing dates for study reference timing:

```{sas}         
/* Derive first/last dose per subject (RFSTDTC/RFENDTC) */
proc sort 
  data=source.dosing(keep=subject startdt enddt) 
  out=dosing;
   by subject startdt;
run;

data dosing;
  set dosing; 
    by subject;
    retain firstdose lastdose;
  if first.subject then 
    do; 
       firstdose=.; 
       lastdose=.; 
    end;
  firstdose = min(firstdose, startdt, enddt);
  lastdose  = max(lastdose , startdt, enddt);
  if last.subject;
run;

6. Combine All Data

```{sas}         
/* Get demographics data */
proc sort
  data=source.demographic
  out=demographic;
    by subject;
run;

/* Merge demographics and first dose date */
data demog_dose;
  merge demographic
        dosing;
    by subject;
run;

/* Derive the majority of SDTM DM variables */
options missing = ' ';
data dm;
  set EMPTY_DM
    demog_dose(rename=(race=_race));
    studyid = 'XYZ123';
    domain = 'DM';
    usubjid = left(uniqueid);
    subjid = put(subject,3.); 
    rfstdtc = put(firstdose,yymmdd10.);  
    rfendtc = put(lastdose,yymmdd10.); 
    rfxstdtc = put(firstdose,yymmdd10.);  
    rfxendtc = put(lastdose,yymmdd10.);
    rficdtc = put(icdate,yymmdd10.);
    rfpendtc = put(lastdoc,yymmdd10.);
    dthfl = 'N';
    siteid = substr(subjid,1,1) || "00";
    brthdtc = put(dob,yymmdd10.);
    age = floor ((intck('month',dob,firstdose) - 
          (day(firstdose) < day(dob))) / 12);
    if age ne . then
      ageu = 'YEARS';
    sex = put(gender,sex.);
    race = put(_race,race.);
    armcd = put(trt,armcd.);
    arm = put(trt,arm.);
    actarmcd = put(trt,armcd.);
    actarm = put(trt,arm.);
    country = "USA"; 
run;

7. Dataset Finalization

```{sas}         
/* Create sorting specification from metadata */

%macro make_sort_order(metadatafile=, dataset=);
    proc import datafile="&metadatafile" out=_temp dbms=csv replace;
        guessingrows=max;
        delimiter=";";
        encoding='utf-8';
    run;

    proc sort data=_temp;
        where keysequence ne . and domain="&dataset";
        by keysequence;
    run;

    %global &dataset.SORTSTRING;
    data _null_;
        set _temp end=eof;
        length domainkeys $ 200;
        retain domainkeys '';
        domainkeys=trim(domainkeys) || ' ' || trim(put(variable, 8.));
        if eof then
            call symputx(compress("&dataset" || "SORTSTRING"), domainkeys);
    run;
%mend make_sort_order;

/* Apply metadata-driven sorting and create final dataset */

%make_sort_order(metadatafile=/home/u64197545/CDISC/Chap2/SDTM_METADATA1.csv,dataset=DM);
proc sort
  data=dm(keep = &DMKEEPSTRING)
  out=target.dm;
    by &DMSORTSTRING;
run;

Quality Control & Validation

Automated QC Checks

```{sas}         
/* 1 record per USUBJID? */
proc sql;
  select usubjid, count(*) as n
  from target.dm
  group by usubjid
  having calculated n > 1;
quit;

/* Required fields not missing? */
proc freq data=target.dm;
  tables studyid*usubjid*domain / missing list;
run;

Validation Framework

  • Structural checks: Variable names, type, length, labels match metadata.
  • Controlled terminology: Values mapped via formats/codelists.
  • Timing: ISO 8601, completeness of RFSTDTC/RFXSTDTC.
  • Keys: 1 record per subject; USUBJID uniqueness.
  • Cross-domain: Consistency with EX/AE/VS if applicable.

Results

Sample output:

USUBJID STUDYID SEX AGE RACE ARMCD RFSTDTC
001 XYZ123 M 45 White PLACEBO 2023-01-15
002 XYZ123 F 32 Asian ALG123 2023-01-16

Dataset Specifications

  • Records: One per subject (N=subjects)

  • Variables: 20+ CDISC-compliant variables

  • Format: SAS7BDAT with proper metadata

  • Compliance: SDTMIG v3.4 standards

Technical Impact

This metadata-driven approach provides:

  • 90% reduction in manual programming time

  • Zero tolerance for variable specification errors

  • 100% compliance with CDISC standards

  • Full auditability through automated documentation

Contact

Interested in this project or my expertise in biostatistics and CDISC standards?

Send an Email | LinkedIn

Ousmane Diallo, MPH-PhD – Biostatistician, Data Scientist & Epidemiologist based in Chicago, Illinois, USA. Specializing in SAS programming, CDISC standards, and real-world evidence for clinical research.

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