178.STEP-BY-STEP SDTM DATASET CREATION GUIDE FOR CLINICAL TRIAL PROGRAMMERS USING SAS | UNIQUE CYBERSECURITY-BASED DEMOGRAPHICS EXAMPLE | PROC DATASETS | PROC PRINT | PROC CONTENTS | PROC EXPORT | SAS MACRO AUTOMATION FOR SDTM COMPLIANCE

STEP-BY-STEP SDTM DATASET CREATION GUIDE FOR CLINICAL TRIAL PROGRAMMERS USING SAS | UNIQUE CYBERSECURITY-BASED DEMOGRAPHICS EXAMPLE | PROC DATASETS | PROC PRINT | PROC CONTENTS | PROC EXPORT | SAS MACRO AUTOMATION FOR SDTM COMPLIANCE

/*Let's walk step-by-step through creating a unique SDTM (Study Data Tabulation Model) dataset*/

Step 1: Understand the SDTM DM Dataset

The Demographics (DM) dataset captures subject-level data such as:

Study ID

Subject ID

Site ID

Age, Sex, Race

Country

Date of Birth

Randomization date (RFSTDTC)

Arm assignment

Unique Subject Identifier (USUBJID)

Step 2: Define Variables

We will use the following variables for the DM dataset:

Variable	Label	Description
STUDYID	Study Identifier	"CYBER001"
DOMAIN	Domain Abbreviation	"DM"
USUBJID	Unique Subject Identifier	"CYBER001-SITEID-SUBJID"
SUBJID	Subject Identifier	Subject number
SITEID	Study Site Identifier	e.g., "101", "102"
SEX	Sex	"M" or "F"
AGE	Age	Integer
AGEU	Age Units	"YEARS"
RACE	Race	Standard categories
COUNTRY	Country	e.g., "INDIA", "USA"
ARM	Description of Planned Arm	e.g., "CyberRelax", "Placebo"
RFSTDTC	Subject Reference Start Date/Time	Randomization date in ISO8601

Step 3: Create the Raw Input Data

Placing 12 unique observations in the Raw Dataset. Let’s create an input dataset in SAS.

data raw_dm;

    infile datalines dlm=',' dsd;

    input SUBJID :$3. SITEID :$3. SEX :$1. AGE RACE :$20. COUNTRY :$10. ARM :$15. RFSTDTC :$10.;

    datalines;

001,101,M,28,Asian,INDIA,CyberRelax,2024-06-01

002,101,F,34,White,USA,Placebo,2024-06-03

003,102,M,41,Black,USA,CyberRelax,2024-06-05

004,102,F,29,Asian,INDIA,CyberRelax,2024-06-06

005,101,M,36,White,USA,Placebo,2024-06-07

006,103,F,26,Other,GERMANY,CyberRelax,2024-06-08

007,103,M,33,Asian,INDIA,Placebo,2024-06-09

008,101,F,45,White,UK,CyberRelax,2024-06-10

009,102,M,38,Black,USA,Placebo,2024-06-11

010,103,F,30,Asian,INDIA,CyberRelax,2024-06-12

011,104,M,27,White,CANADA,Placebo,2024-06-13

012,104,F,31,Other,JAPAN,CyberRelax,2024-06-14

;

run;

proc print;run;

Output:

Obs	SUBJID	SITEID	SEX	AGE	RACE	COUNTRY	ARM	RFSTDTC
1	001	101	M	28	Asian	INDIA	CyberRelax	2024-06-01
2	002	101	F	34	White	USA	Placebo	2024-06-03
3	003	102	M	41	Black	USA	CyberRelax	2024-06-05
4	004	102	F	29	Asian	INDIA	CyberRelax	2024-06-06
5	005	101	M	36	White	USA	Placebo	2024-06-07
6	006	103	F	26	Other	GERMANY	CyberRelax	2024-06-08
7	007	103	M	33	Asian	INDIA	Placebo	2024-06-09
8	008	101	F	45	White	UK	CyberRelax	2024-06-10
9	009	102	M	38	Black	USA	Placebo	2024-06-11
10	010	103	F	30	Asian	INDIA	CyberRelax	2024-06-12
11	011	104	M	27	White	CANADA	Placebo	2024-06-13
12	012	104	F	31	Other	JAPAN	CyberRelax	2024-06-14

Step 4: Define a SAS Macro to Transform to SDTM Format

Add STUDYID as “CYBER001”

Add DOMAIN as “DM”

Create USUBJID = STUDYID || "-" || SITEID || "-" || SUBJID

Add AGEU as “YEARS”

%macro create_sdtm_dm(input=, output=);

    data &output.;

        set &input.;

        length STUDYID $10 DOMAIN $2 USUBJID $25 AGEU $5;

        STUDYID = "CYBER001";

        DOMAIN  = "DM";

        USUBJID = catx("-", STUDYID, SITEID, SUBJID);

        AGEU    = "YEARS";

        label 

            STUDYID  = "Study Identifier"

            DOMAIN   = "Domain Abbreviation"

            USUBJID  = "Unique Subject Identifier"

            SUBJID   = "Subject Identifier for the Study"

            SITEID   = "Study Site Identifier"

            SEX      = "Sex"

            AGE      = "Age"

            AGEU     = "Age Units"

            RACE     = "Race"

            COUNTRY  = "Country"

            ARM      = "Description of Planned Arm"

            RFSTDTC  = "Subject Reference Start Date/Time";

    run;

    proc print;run;

%mend;

 Step 5: Execute the Macro

 %create_sdtm_dm(input=raw_dm, output=sdtm_dm);

Output:

Obs	SUBJID	SITEID	SEX	AGE	RACE	COUNTRY	ARM	RFSTDTC	STUDYID	DOMAIN	USUBJID	AGEU
1	001	101	M	28	Asian	INDIA	CyberRelax	2024-06-01	CYBER001	DM	CYBER001-101-001	YEARS
2	002	101	F	34	White	USA	Placebo	2024-06-03	CYBER001	DM	CYBER001-101-002	YEARS
3	003	102	M	41	Black	USA	CyberRelax	2024-06-05	CYBER001	DM	CYBER001-102-003	YEARS
4	004	102	F	29	Asian	INDIA	CyberRelax	2024-06-06	CYBER001	DM	CYBER001-102-004	YEARS
5	005	101	M	36	White	USA	Placebo	2024-06-07	CYBER001	DM	CYBER001-101-005	YEARS
6	006	103	F	26	Other	GERMANY	CyberRelax	2024-06-08	CYBER001	DM	CYBER001-103-006	YEARS
7	007	103	M	33	Asian	INDIA	Placebo	2024-06-09	CYBER001	DM	CYBER001-103-007	YEARS
8	008	101	F	45	White	UK	CyberRelax	2024-06-10	CYBER001	DM	CYBER001-101-008	YEARS
9	009	102	M	38	Black	USA	Placebo	2024-06-11	CYBER001	DM	CYBER001-102-009	YEARS
10	010	103	F	30	Asian	INDIA	CyberRelax	2024-06-12	CYBER001	DM	CYBER001-103-010	YEARS
11	011	104	M	27	White	CANADA	Placebo	2024-06-13	CYBER001	DM	CYBER001-104-011	YEARS
12	012	104	F	31	Other	JAPAN	CyberRelax	2024-06-14	CYBER001	DM	CYBER001-104-012	YEARS

After execution, the sdtm_dm dataset will contain transformed and SDTM-compliant data.

Step 6: Review the SDTM Dataset

proc print data=sdtm_dm label noobs;

run;

Output:

Subject Identifier for the Study	Study Site Identifier	Sex	Age	Race	Country	Description of Planned Arm	Subject Reference Start Date/Time	Study Identifier	Domain Abbreviation	Unique Subject Identifier	Age Units
001	101	M	28	Asian	INDIA	CyberRelax	2024-06-01	CYBER001	DM	CYBER001-101-001	YEARS
002	101	F	34	White	USA	Placebo	2024-06-03	CYBER001	DM	CYBER001-101-002	YEARS
003	102	M	41	Black	USA	CyberRelax	2024-06-05	CYBER001	DM	CYBER001-102-003	YEARS
004	102	F	29	Asian	INDIA	CyberRelax	2024-06-06	CYBER001	DM	CYBER001-102-004	YEARS
005	101	M	36	White	USA	Placebo	2024-06-07	CYBER001	DM	CYBER001-101-005	YEARS
006	103	F	26	Other	GERMANY	CyberRelax	2024-06-08	CYBER001	DM	CYBER001-103-006	YEARS
007	103	M	33	Asian	INDIA	Placebo	2024-06-09	CYBER001	DM	CYBER001-103-007	YEARS
008	101	F	45	White	UK	CyberRelax	2024-06-10	CYBER001	DM	CYBER001-101-008	YEARS
009	102	M	38	Black	USA	Placebo	2024-06-11	CYBER001	DM	CYBER001-102-009	YEARS
010	103	F	30	Asian	INDIA	CyberRelax	2024-06-12	CYBER001	DM	CYBER001-103-010	YEARS
011	104	M	27	White	CANADA	Placebo	2024-06-13	CYBER001	DM	CYBER001-104-011	YEARS
012	104	F	31	Other	JAPAN	CyberRelax	2024-06-14	CYBER001	DM	CYBER001-104-012	YEARS

Step 7: SDTM Compliance Checks

7.1 Variable Order

%macro reorder_dm(dataset=, out=);

    data &out.;

    retain STUDYID DOMAIN USUBJID SUBJID SITEID SEX AGE AGEU RACE   COUNTRY               ARM RFSTDTC;

        set &dataset.;

    run;

   proc print;run;

%mend;

%reorder_dm(dataset=sdtm_dm, out=sdtm_dm_final);

Output:

Obs	STUDYID	DOMAIN	USUBJID	SUBJID	SITEID	SEX	AGE	AGEU	RACE	COUNTRY	ARM	RFSTDTC
1	CYBER001	DM	CYBER001-101-001	001	101	M	28	YEARS	Asian	INDIA	CyberRelax	2024-06-01
2	CYBER001	DM	CYBER001-101-002	002	101	F	34	YEARS	White	USA	Placebo	2024-06-03
3	CYBER001	DM	CYBER001-102-003	003	102	M	41	YEARS	Black	USA	CyberRelax	2024-06-05
4	CYBER001	DM	CYBER001-102-004	004	102	F	29	YEARS	Asian	INDIA	CyberRelax	2024-06-06
5	CYBER001	DM	CYBER001-101-005	005	101	M	36	YEARS	White	USA	Placebo	2024-06-07
6	CYBER001	DM	CYBER001-103-006	006	103	F	26	YEARS	Other	GERMANY	CyberRelax	2024-06-08
7	CYBER001	DM	CYBER001-103-007	007	103	M	33	YEARS	Asian	INDIA	Placebo	2024-06-09
8	CYBER001	DM	CYBER001-101-008	008	101	F	45	YEARS	White	UK	CyberRelax	2024-06-10
9	CYBER001	DM	CYBER001-102-009	009	102	M	38	YEARS	Black	USA	Placebo	2024-06-11
10	CYBER001	DM	CYBER001-103-010	010	103	F	30	YEARS	Asian	INDIA	CyberRelax	2024-06-12
11	CYBER001	DM	CYBER001-104-011	011	104	M	27	YEARS	White	CANADA	Placebo	2024-06-13
12	CYBER001	DM	CYBER001-104-012	012	104	F	31	YEARS	Other	JAPAN	CyberRelax	2024-06-14

7.2 Length Check

proc contents data=sdtm_dm_final;

run;

Output:

                                                               The CONTENTS Procedure

Data Set Name	WORK.SDTM_DM_FINAL	Observations	12
Member Type	DATA	Variables	12
Engine	V9	Indexes	0
Created	14/09/2015 00:24:22	Observation Length	112
Last Modified	14/09/2015 00:24:22	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	WINDOWS_64
Encoding	wlatin1 Western (Windows)

Engine/Host Dependent Information
Data Set Page Size	65536
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	584
Obs in First Data Page	12
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD1524_DESKTOP-QFAA4KV_\sdtm_dm_final.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Label
7	AGE	Num	8	Age
8	AGEU	Char	5	Age Units
11	ARM	Char	15	Description of Planned Arm
10	COUNTRY	Char	10	Country
2	DOMAIN	Char	2	Domain Abbreviation
9	RACE	Char	20	Race
12	RFSTDTC	Char	10	Subject Reference Start Date/Time
6	SEX	Char	1	Sex
5	SITEID	Char	3	Study Site Identifier
1	STUDYID	Char	10	Study Identifier
4	SUBJID	Char	3	Subject Identifier for the Study
3	USUBJID	Char	25	Unique Subject Identifier

Step 8: Export the Dataset to XPORT Format

XPT is used for FDA submission.

libname outlib xport 'path/sdtm_dm_final.xpt';

data outlib.DM;

    set sdtm_dm_final;

run;

Overall Summary:

1.Defined the goal: create a unique SDTM-compliant DM dataset.

2.Created raw input with realistic, cybersecurity-themed trial data.

3.Wrote a macro to automate transformation to SDTM format.

4.Executed and validated the macro.

5.Reordered variables for CDISC compliance.

6.Exported the dataset to an XPORT format.

PRACTICE AND COMMENT YOUR CODE: 

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