340.Is SDTM DM quality control in SAS the key to avoiding last-minute FDA rejection risks?

Is SDTM DM quality control in SAS the key to avoiding last-minute FDA rejection risks?

SDTM DM PRACTICE DATASET

 One record per subject

data work.sdtm_dm;

    length

        STUDYID   $12

        DOMAIN    $2

        USUBJID   $25

        SUBJID    $10

        SITEID    $5

        COUNTRY   $15

        SEX       $1

        RACE      $20

        ETHNIC    $30

        AGE       8

        AGEU      $10

        ARMCD     $8

        ARM       $20

        ACTARMCD  $8

        ACTARM    $20

        INVID     $10

        INVNAM    $30

        BRTHDTC   $10

        RFSTDTC   $10

        RFENDTC   $10

        DMDTC     $10

    ;

    input

        STUDYID $

        USUBJID $

        SUBJID $

        SITEID $

        COUNTRY $

        SEX $

        RACE $

        ETHNIC $

        AGE

        AGEU $

        ARMCD $

        ARM $

        ACTARMCD $

        ACTARM $

        BRTHDTC $

        RFSTDTC $

        RFENDTC $

        DMDTC $

        INVID $

        INVNAM $

    ;

    DOMAIN = "DM";

    drop ACTARMCD ACTARM;

datalines;

STUDY001 STUDY001-IND-001 001 1001 INDIA M ASIAN NOT_HISPANIC 34 YEARS TRT01 DRUG_A TRT01 DRUG_A 1990-05-10 2023-01-10 2023-06-10 2023-01-05 INV01 DR_RAO

STUDY001 STUDY001-IND-002 002 1001 INDIA F ASIAN NOT_HISPANIC 29 YEARS TRT02 DRUG_B TRT02 DRUG_B 1995-08-21 2023-01-12 2023-06-15 2023-01-07 INV01 DR_RAO

STUDY001 STUDY001-USA-003 003 2001 USA M WHITE NOT_HISPANIC 45 YEARS TRT01 DRUG_A TRT01 DRUG_A 1978-03-14 2023-02-01 2023-07-01 2023-01-25 INV02 DR_SMITH

STUDY001 STUDY001-USA-004 004 2001 USA F BLACK OR_AFRICAN_AMERICAN_HISPANIC 38 YEARS TRT02 DRUG_B TRT02 DRUG_B 1986-11-02 2023-02-05 2023-07-10 2023-01-28 INV02 DR_SMITH

STUDY001 STUDY001-JPN-005 005 3001 JAPAN M ASIAN NOT_HISPANIC 52 YEARS TRT01 DRUG_A TRT01 DRUG_A 1971-07-09 2023-03-01 2023-08-01 2023-02-20 INV03 DR_TANAKA

STUDY001 STUDY001-JPN-006 006 3001 JAPAN F ASIAN NOT_HISPANIC 47 YEARS TRT02 DRUG_B TRT02 DRUG_B 1976-01-18 2023-03-05 2023-08-05 2023-02-22 INV03 DR_TANAKA

STUDY001 STUDY001-UK-007 007 4001 UK M WHITE NOT_HISPANIC 60 YEARS TRT01 DRUG_A TRT01 DRUG_A 1963-09-25 2023-04-01 2023-09-01 2023-03-18 INV04 DR_BROWN

STUDY001 STUDY001-UK-008 008 4001 UK F WHITE NOT_HISPANIC 55 YEARS TRT02 DRUG_B TRT02 DRUG_B 1968-12-11 2023-04-03 2023-09-05 2023-03-20 INV04 DR_BROWN

STUDY001 STUDY001-AUS-009 009 5001 AUSTRALIA M WHITE NOT_HISPANIC 41 YEARS TRT01 DRUG_A TRT01 DRUG_A 1982-06-06 2023-05-01 2023-10-01 2023-04-15 INV05 DR_LEE

STUDY001 STUDY001-AUS-010 010 5001 AUSTRALIA F WHITE NOT_HISPANIC 36 YEARS TRT02 DRUG_B TRT02 DRUG_B 1987-02-19 2023-05-03 2023-10-05 2023-04-17 INV05 DR_LEE

STUDY001 STUDY001-CAN-011 011 6001 CANADA M WHITE NOT_HISPANIC 49 YEARS TRT01 DRUG_A TRT01 DRUG_A 1974-10-30 2023-06-01 2023-11-01 2023-05-20 INV06 DR_GREEN

STUDY001 STUDY001-CAN-012 012 6001 CANADA F WHITE NOT_HISPANIC 44 YEARS TRT02 DRUG_B TRT02 DRUG_B 1979-04-05 2023-06-05 2023-11-05 2023-05-22 INV06 DR_GREEN

;

run;

proc print data=sdtm_dm;

run;

Obs	STUDYID	DOMAIN	USUBJID	SUBJID	SITEID	COUNTRY	SEX	RACE	ETHNIC	AGE	AGEU	ARMCD	ARM	INVID	INVNAM	BRTHDTC	RFSTDTC	RFENDTC	DMDTC
1	STUDY001	DM	STUDY001-IND-001	001	1001	INDIA	M	ASIAN	NOT_HISPANIC	34	YEARS	TRT01	DRUG_A	INV01	DR_RAO	1990-05-10	2023-01-10	2023-06-10	2023-01-05
2	STUDY001	DM	STUDY001-IND-002	002	1001	INDIA	F	ASIAN	NOT_HISPANIC	29	YEARS	TRT02	DRUG_B	INV01	DR_RAO	1995-08-21	2023-01-12	2023-06-15	2023-01-07
3	STUDY001	DM	STUDY001-USA-003	003	2001	USA	M	WHITE	NOT_HISPANIC	45	YEARS	TRT01	DRUG_A	INV02	DR_SMITH	1978-03-14	2023-02-01	2023-07-01	2023-01-25
4	STUDY001	DM	STUDY001-USA-004	004	2001	USA	F	BLACK	OR_AFRICAN_AMERICAN_HISPANIC	38	YEARS	TRT02	DRUG_B	INV02	DR_SMITH	1986-11-02	2023-02-05	2023-07-10	2023-01-28
5	STUDY001	DM	STUDY001-JPN-005	005	3001	JAPAN	M	ASIAN	NOT_HISPANIC	52	YEARS	TRT01	DRUG_A	INV03	DR_TANAKA	1971-07-09	2023-03-01	2023-08-01	2023-02-20
6	STUDY001	DM	STUDY001-JPN-006	006	3001	JAPAN	F	ASIAN	NOT_HISPANIC	47	YEARS	TRT02	DRUG_B	INV03	DR_TANAKA	1976-01-18	2023-03-05	2023-08-05	2023-02-22
7	STUDY001	DM	STUDY001-UK-007	007	4001	UK	M	WHITE	NOT_HISPANIC	60	YEARS	TRT01	DRUG_A	INV04	DR_BROWN	1963-09-25	2023-04-01	2023-09-01	2023-03-18
8	STUDY001	DM	STUDY001-UK-008	008	4001	UK	F	WHITE	NOT_HISPANIC	55	YEARS	TRT02	DRUG_B	INV04	DR_BROWN	1968-12-11	2023-04-03	2023-09-05	2023-03-20
9	STUDY001	DM	STUDY001-AUS-009	009	5001	AUSTRALIA	M	WHITE	NOT_HISPANIC	41	YEARS	TRT01	DRUG_A	INV05	DR_LEE	1982-06-06	2023-05-01	2023-10-01	2023-04-15
10	STUDY001	DM	STUDY001-AUS-010	010	5001	AUSTRALIA	F	WHITE	NOT_HISPANIC	36	YEARS	TRT02	DRUG_B	INV05	DR_LEE	1987-02-19	2023-05-03	2023-10-05	2023-04-17
11	STUDY001	DM	STUDY001-CAN-011	011	6001	CANADA	M	WHITE	NOT_HISPANIC	49	YEARS	TRT01	DRUG_A	INV06	DR_GREEN	1974-10-30	2023-06-01	2023-11-01	2023-05-20
12	STUDY001	DM	STUDY001-CAN-012	012	6001	CANADA	F	WHITE	NOT_HISPANIC	44	YEARS	TRT02	DRUG_B	INV06	DR_GREEN	1979-04-05	2023-06-05	2023-11-05	2023-05-22

HOW TO PRACTICE WITH THIS DATASET

A. BASIC CHECKS

proc contents data=sdtm_dm;

run;

OUTPUT:

The CONTENTS Procedure

Data Set Name	WORK.SDTM_DM	Observations	12
Member Type	DATA	Variables	19
Engine	V9	Indexes	0
Created	12/16/2025 07:10:23	Observation Length	248
Last Modified	12/16/2025 07:10:23	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	528
Obs in First Data Page	12
Number of Data Set Repairs	0
Filename	/saswork/SAS_workB6BB0000902C_odaws01-apse1-2.oda.sas.com/SAS_workF09A0000902C_odaws01-apse1-2.oda.sas.com/sdtm_dm.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	1214119
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len
10	AGE	Num	8
11	AGEU	Char	10
13	ARM	Char	20
12	ARMCD	Char	8
16	BRTHDTC	Char	10
6	COUNTRY	Char	15
19	DMDTC	Char	10
2	DOMAIN	Char	2
9	ETHNIC	Char	30
14	INVID	Char	10
15	INVNAM	Char	30
8	RACE	Char	20
18	RFENDTC	Char	10
17	RFSTDTC	Char	10
7	SEX	Char	1
5	SITEID	Char	5
1	STUDYID	Char	12
4	SUBJID	Char	10
3	USUBJID	Char	25

proc print data=sdtm_dm(obs=5);

run;

OUTPUT:

Obs	STUDYID	DOMAIN	USUBJID	SUBJID	SITEID	COUNTRY	SEX	RACE	ETHNIC	AGE	AGEU	ARMCD	ARM	INVID	INVNAM	BRTHDTC	RFSTDTC	RFENDTC	DMDTC
1	STUDY001	DM	STUDY001-IND-001	001	1001	INDIA	M	ASIAN	NOT_HISPANIC	34	YEARS	TRT01	DRUG_A	INV01	DR_RAO	1990-05-10	2023-01-10	2023-06-10	2023-01-05
2	STUDY001	DM	STUDY001-IND-002	002	1001	INDIA	F	ASIAN	NOT_HISPANIC	29	YEARS	TRT02	DRUG_B	INV01	DR_RAO	1995-08-21	2023-01-12	2023-06-15	2023-01-07
3	STUDY001	DM	STUDY001-USA-003	003	2001	USA	M	WHITE	NOT_HISPANIC	45	YEARS	TRT01	DRUG_A	INV02	DR_SMITH	1978-03-14	2023-02-01	2023-07-01	2023-01-25
4	STUDY001	DM	STUDY001-USA-004	004	2001	USA	F	BLACK	OR_AFRICAN_AMERICAN_HISPANIC	38	YEARS	TRT02	DRUG_B	INV02	DR_SMITH	1986-11-02	2023-02-05	2023-07-10	2023-01-28
5	STUDY001	DM	STUDY001-JPN-005	005	3001	JAPAN	M	ASIAN	NOT_HISPANIC	52	YEARS	TRT01	DRUG_A	INV03	DR_TANAKA	1971-07-09	2023-03-01	2023-08-01	2023-02-20

B. SUBJECT COUNT BY SEX

proc freq data=sdtm_dm;

    tables SEX;

run;

OUTPUT:

The FREQ Procedure

SEX	Frequency	Percent	Cumulative Frequency	Cumulative Percent
F	6	50.00	6	50.00
M	6	50.00	12	100.00

C. AGE SUMMARY

proc means data=sdtm_dm min max mean;

    var AGE;

run;

OUTPUT:

The MEANS Procedure

Analysis Variable : AGE
Minimum	Maximum	Mean
29.0000000	60.0000000	44.1666667

D. COUNTRY-WISE SUBJECT DISTRIBUTION

proc freq data=sdtm_dm;

    tables COUNTRY;

run;

OUTPUT:

The FREQ Procedure

COUNTRY	Frequency	Percent	Cumulative Frequency	Cumulative Percent
AUSTRALIA	2	16.67	2	16.67
CANADA	2	16.67	4	33.33
INDIA	2	16.67	6	50.00
JAPAN	2	16.67	8	66.67
UK	2	16.67	10	83.33
USA	2	16.67	12	100.00

E. CHECK ONE RECORD PER SUBJECT

proc sort data=sdtm_dm out=dm_sorted;

    by USUBJID;

run;

proc print data=dm_sorted;

run;

OUTPUT:

Obs	STUDYID	DOMAIN	USUBJID	SUBJID	SITEID	COUNTRY	SEX	RACE	ETHNIC	AGE	AGEU	ARMCD	ARM	INVID	INVNAM	BRTHDTC	RFSTDTC	RFENDTC	DMDTC
1	STUDY001	DM	STUDY001-AUS-009	009	5001	AUSTRALIA	M	WHITE	NOT_HISPANIC	41	YEARS	TRT01	DRUG_A	INV05	DR_LEE	1982-06-06	2023-05-01	2023-10-01	2023-04-15
2	STUDY001	DM	STUDY001-AUS-010	010	5001	AUSTRALIA	F	WHITE	NOT_HISPANIC	36	YEARS	TRT02	DRUG_B	INV05	DR_LEE	1987-02-19	2023-05-03	2023-10-05	2023-04-17
3	STUDY001	DM	STUDY001-CAN-011	011	6001	CANADA	M	WHITE	NOT_HISPANIC	49	YEARS	TRT01	DRUG_A	INV06	DR_GREEN	1974-10-30	2023-06-01	2023-11-01	2023-05-20
4	STUDY001	DM	STUDY001-CAN-012	012	6001	CANADA	F	WHITE	NOT_HISPANIC	44	YEARS	TRT02	DRUG_B	INV06	DR_GREEN	1979-04-05	2023-06-05	2023-11-05	2023-05-22
5	STUDY001	DM	STUDY001-IND-001	001	1001	INDIA	M	ASIAN	NOT_HISPANIC	34	YEARS	TRT01	DRUG_A	INV01	DR_RAO	1990-05-10	2023-01-10	2023-06-10	2023-01-05
6	STUDY001	DM	STUDY001-IND-002	002	1001	INDIA	F	ASIAN	NOT_HISPANIC	29	YEARS	TRT02	DRUG_B	INV01	DR_RAO	1995-08-21	2023-01-12	2023-06-15	2023-01-07
7	STUDY001	DM	STUDY001-JPN-005	005	3001	JAPAN	M	ASIAN	NOT_HISPANIC	52	YEARS	TRT01	DRUG_A	INV03	DR_TANAKA	1971-07-09	2023-03-01	2023-08-01	2023-02-20
8	STUDY001	DM	STUDY001-JPN-006	006	3001	JAPAN	F	ASIAN	NOT_HISPANIC	47	YEARS	TRT02	DRUG_B	INV03	DR_TANAKA	1976-01-18	2023-03-05	2023-08-05	2023-02-22
9	STUDY001	DM	STUDY001-UK-007	007	4001	UK	M	WHITE	NOT_HISPANIC	60	YEARS	TRT01	DRUG_A	INV04	DR_BROWN	1963-09-25	2023-04-01	2023-09-01	2023-03-18
10	STUDY001	DM	STUDY001-UK-008	008	4001	UK	F	WHITE	NOT_HISPANIC	55	YEARS	TRT02	DRUG_B	INV04	DR_BROWN	1968-12-11	2023-04-03	2023-09-05	2023-03-20
11	STUDY001	DM	STUDY001-USA-003	003	2001	USA	M	WHITE	NOT_HISPANIC	45	YEARS	TRT01	DRUG_A	INV02	DR_SMITH	1978-03-14	2023-02-01	2023-07-01	2023-01-25
12	STUDY001	DM	STUDY001-USA-004	004	2001	USA	F	BLACK	OR_AFRICAN_AMERICAN_HISPANIC	38	YEARS	TRT02	DRUG_B	INV02	DR_SMITH	1986-11-02	2023-02-05	2023-07-10	2023-01-28

data dm_dup;

    set dm_sorted;

    by USUBJID;

    if not (first.USUBJID and last.USUBJID);

run;

proc print data=dm_dup;

    title "DUPLICATE USUBJID CHECK";

run;

LOG:

NOTE: No observations in data set WORK.DM_DUP.

F. CHECK MISSING CRITICAL VARIABLES

proc means data=sdtm_dm n nmiss;

    var AGE;

run;

OUTPUT:

The MEANS Procedure

Analysis Variable : AGE
N	N Miss
12	0

proc freq data=sdtm_dm;

    tables SEX RACE ETHNIC COUNTRY / missing;

run;

OUTPUT:

The FREQ Procedure

SEX	Frequency	Percent	Cumulative Frequency	Cumulative Percent
F	6	50.00	6	50.00
M	6	50.00	12	100.00

RACE	Frequency	Percent	Cumulative Frequency	Cumulative Percent
ASIAN	4	33.33	4	33.33
BLACK	1	8.33	5	41.67
WHITE	7	58.33	12	100.00

ETHNIC	Frequency	Percent	Cumulative Frequency	Cumulative Percent
NOT_HISPANIC	11	91.67	11	91.67
OR_AFRICAN_AMERICAN_HISPANIC	1	8.33	12	100.00

COUNTRY	Frequency	Percent	Cumulative Frequency	Cumulative Percent
AUSTRALIA	2	16.67	2	16.67
CANADA	2	16.67	4	33.33
INDIA	2	16.67	6	50.00
JAPAN	2	16.67	8	66.67
UK	2	16.67	10	83.33
USA	2	16.67	12	100.00

G. VALIDATE AGE CALCULATION USING BIRTH DATE

data dm_age_check;

    set sdtm_dm;

    /* Convert ISO 8601 character dates to numeric SAS dates */

    if not missing(BRTHDTC) then

        BRTHDTN = input(BRTHDTC, yymmdd10.);

    if not missing(RFSTDTC) then

        RFSTDTN = input(RFSTDTC, yymmdd10.);

    /* Calculate age only when both dates are valid */

    if not missing(BRTHDTN) and not missing(RFSTDTN) then

        AGE_CALC = intck('year', BRTHDTN, RFSTDTN, 'c');

    label AGE_CALC = 'Calculated Age at Reference Start Date';

run;

proc print data=dm_age_check;

    var USUBJID AGE AGE_CALC BRTHDTC RFSTDTC;

run;

OUTPUT:

Obs	USUBJID	AGE	AGE_CALC	BRTHDTC	RFSTDTC
1	STUDY001-IND-001	34	32	1990-05-10	2023-01-10
2	STUDY001-IND-002	29	27	1995-08-21	2023-01-12
3	STUDY001-USA-003	45	44	1978-03-14	2023-02-01
4	STUDY001-USA-004	38	36	1986-11-02	2023-02-05
5	STUDY001-JPN-005	52	51	1971-07-09	2023-03-01
6	STUDY001-JPN-006	47	47	1976-01-18	2023-03-05
7	STUDY001-UK-007	60	59	1963-09-25	2023-04-01
8	STUDY001-UK-008	55	54	1968-12-11	2023-04-03
9	STUDY001-AUS-009	41	40	1982-06-06	2023-05-01
10	STUDY001-AUS-010	36	36	1987-02-19	2023-05-03
11	STUDY001-CAN-011	49	48	1974-10-30	2023-06-01
12	STUDY001-CAN-012	44	44	1979-04-05	2023-06-05

H. CHECK DATE ORDER CONSISTENCY

data dm_date_check;

    set sdtm_dm;

    if RFSTDTC > RFENDTC then FLAG_DATE = "ERROR";

run;

proc print data=dm_date_check;

    where FLAG_DATE = "ERROR";

    var USUBJID RFSTDTC RFENDTC;

run;

LOG:

NOTE: No observations were selected from data set WORK.DM_DATE_CHECK.

I. COUNTRY AND SITE CROSS-CHECK

proc freq data=sdtm_dm;

    tables COUNTRY*SITEID / nocol norow;

run;

OUTPUT:

The FREQ Procedure

Frequency Percent

Table of COUNTRY by SITEID COUNTRY SITEID 1001 2001 3001 4001 5001 6001 Total AUSTRALIA 0 0.00 0 0.00 0 0.00 0 0.00 2 16.67 0 0.00 2 16.67 CANADA 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 2 16.67 2 16.67 INDIA 2 16.67 0 0.00 0 0.00 0 0.00 0 0.00 0 0.00 2 16.67 JAPAN 0 0.00 0 0.00 2 16.67 0 0.00 0 0.00 0 0.00 2 16.67 UK 0 0.00 0 0.00 0 0.00 2 16.67 0 0.00 0 0.00 2 16.67 USA 0 0.00 2 16.67 0 0.00 0 0.00 0 0.00 0 0.00 2 16.67 Total 2 16.67 2 16.67 2 16.67 2 16.67 2 16.67 2 16.67 12 100.00

J. SEX VS RACE DISTRIBUTION (QA CHECK)

proc freq data=sdtm_dm;

    tables SEX*RACE / missing;

run;

OUTPUT:

The FREQ Procedure

Frequency Percent Row Pct Col Pct

Table of SEX by RACE SEX RACE ASIAN BLACK WHITE Total F 2 16.67 33.33 50.00 1 8.33 16.67 100.00 3 25.00 50.00 42.86 6 50.00     M 2 16.67 33.33 50.00 0 0.00 0.00 0.00 4 33.33 66.67 57.14 6 50.00     Total 4 33.33 1 8.33 7 58.33 12 100.00

K. POPULATION COUNTS BY ARM

proc freq data=sdtm_dm;

    tables ARMCD;

run;

OUTPUT:

The FREQ Procedure

ARMCD	Frequency	Percent	Cumulative Frequency	Cumulative Percent
TRT01	6	50.00	6	50.00
TRT02	6	50.00	12	100.00

L. MIN / MAX STUDY DATES

proc sql;

    select

        min(input(RFSTDTC, yymmdd10.)) as Min_RFSTD format=date9.,

        max(input(RFSTDTC, yymmdd10.)) as Max_RFSTD format=date9.,

        min(input(RFENDTC, yymmdd10.)) as Min_RFEND format=date9.,

        max(input(RFENDTC, yymmdd10.)) as Max_RFEND format=date9.

    from sdtm_dm;

quit;

OUTPUT:

Min_RFSTD	Max_RFSTD	Min_RFEND	Max_RFEND
10JAN2023	05JUN2023	10JUN2023	05NOV2023

M. CREATE A DERIVED AGE GROUP (PRACTICE STEP)

data dm_agegrp;

    set sdtm_dm;

    length AGEGRP $10;

    if AGE < 30 then AGEGRP = "<30";

    else if 30 <= AGE < 50 then AGEGRP = "30-49";

    else if AGE >= 50 then AGEGRP = "50+";

run;

Proc print data=dm_agegrp;

run;

OUTPUT:

Obs	STUDYID	DOMAIN	USUBJID	SUBJID	SITEID	COUNTRY	SEX	RACE	ETHNIC	AGE	AGEU	ARMCD	ARM	INVID	INVNAM	BRTHDTC	RFSTDTC	RFENDTC	DMDTC	AGEGRP
1	STUDY001	DM	STUDY001-IND-001	001	1001	INDIA	M	ASIAN	NOT_HISPANIC	34	YEARS	TRT01	DRUG_A	INV01	DR_RAO	1990-05-10	2023-01-10	2023-06-10	2023-01-05	30-49
2	STUDY001	DM	STUDY001-IND-002	002	1001	INDIA	F	ASIAN	NOT_HISPANIC	29	YEARS	TRT02	DRUG_B	INV01	DR_RAO	1995-08-21	2023-01-12	2023-06-15	2023-01-07	<30
3	STUDY001	DM	STUDY001-USA-003	003	2001	USA	M	WHITE	NOT_HISPANIC	45	YEARS	TRT01	DRUG_A	INV02	DR_SMITH	1978-03-14	2023-02-01	2023-07-01	2023-01-25	30-49
4	STUDY001	DM	STUDY001-USA-004	004	2001	USA	F	BLACK	OR_AFRICAN_AMERICAN_HISPANIC	38	YEARS	TRT02	DRUG_B	INV02	DR_SMITH	1986-11-02	2023-02-05	2023-07-10	2023-01-28	30-49
5	STUDY001	DM	STUDY001-JPN-005	005	3001	JAPAN	M	ASIAN	NOT_HISPANIC	52	YEARS	TRT01	DRUG_A	INV03	DR_TANAKA	1971-07-09	2023-03-01	2023-08-01	2023-02-20	50+
6	STUDY001	DM	STUDY001-JPN-006	006	3001	JAPAN	F	ASIAN	NOT_HISPANIC	47	YEARS	TRT02	DRUG_B	INV03	DR_TANAKA	1976-01-18	2023-03-05	2023-08-05	2023-02-22	30-49
7	STUDY001	DM	STUDY001-UK-007	007	4001	UK	M	WHITE	NOT_HISPANIC	60	YEARS	TRT01	DRUG_A	INV04	DR_BROWN	1963-09-25	2023-04-01	2023-09-01	2023-03-18	50+
8	STUDY001	DM	STUDY001-UK-008	008	4001	UK	F	WHITE	NOT_HISPANIC	55	YEARS	TRT02	DRUG_B	INV04	DR_BROWN	1968-12-11	2023-04-03	2023-09-05	2023-03-20	50+
9	STUDY001	DM	STUDY001-AUS-009	009	5001	AUSTRALIA	M	WHITE	NOT_HISPANIC	41	YEARS	TRT01	DRUG_A	INV05	DR_LEE	1982-06-06	2023-05-01	2023-10-01	2023-04-15	30-49
10	STUDY001	DM	STUDY001-AUS-010	010	5001	AUSTRALIA	F	WHITE	NOT_HISPANIC	36	YEARS	TRT02	DRUG_B	INV05	DR_LEE	1987-02-19	2023-05-03	2023-10-05	2023-04-17	30-49
11	STUDY001	DM	STUDY001-CAN-011	011	6001	CANADA	M	WHITE	NOT_HISPANIC	49	YEARS	TRT01	DRUG_A	INV06	DR_GREEN	1974-10-30	2023-06-01	2023-11-01	2023-05-20	30-49
12	STUDY001	DM	STUDY001-CAN-012	012	6001	CANADA	F	WHITE	NOT_HISPANIC	44	YEARS	TRT02	DRUG_B	INV06	DR_GREEN	1979-04-05	2023-06-05	2023-11-05	2023-05-22	30-49

proc freq data=dm_agegrp;

    tables AGEGRP;

run;

OUTPUT:

The FREQ Procedure

AGEGRP	Frequency	Percent	Cumulative Frequency	Cumulative Percent
30-49	8	66.67	8	66.67
50+	3	25.00	11	91.67
<30	1	8.33	12	100.00

N. CHECK DEATH FLAG LOGIC

data dm_death_check;

    set sdtm_dm;

    if DTHFL = "Y" and DTHDTC = . then FLAG = "MISSING DTHDTC";

run;

proc print data=dm_death_check;

    where FLAG ne "";

run;

LOG:

NOTE: No observations were selected from data set WORK.DM_DEATH_CHECK.

O. SORTING DM AS PER SDTM RULE

proc sort data=sdtm_dm;

    by STUDYID USUBJID;

run;

Proc print data=sdtm_dm;

run;

OUTPUT:

Obs	STUDYID	DOMAIN	USUBJID	SUBJID	SITEID	COUNTRY	SEX	RACE	ETHNIC	AGE	AGEU	ARMCD	ARM	INVID	INVNAM	BRTHDTC	RFSTDTC	RFENDTC	DMDTC
1	STUDY001	DM	STUDY001-AUS-009	009	5001	AUSTRALIA	M	WHITE	NOT_HISPANIC	41	YEARS	TRT01	DRUG_A	INV05	DR_LEE	1982-06-06	2023-05-01	2023-10-01	2023-04-15
2	STUDY001	DM	STUDY001-AUS-010	010	5001	AUSTRALIA	F	WHITE	NOT_HISPANIC	36	YEARS	TRT02	DRUG_B	INV05	DR_LEE	1987-02-19	2023-05-03	2023-10-05	2023-04-17
3	STUDY001	DM	STUDY001-CAN-011	011	6001	CANADA	M	WHITE	NOT_HISPANIC	49	YEARS	TRT01	DRUG_A	INV06	DR_GREEN	1974-10-30	2023-06-01	2023-11-01	2023-05-20
4	STUDY001	DM	STUDY001-CAN-012	012	6001	CANADA	F	WHITE	NOT_HISPANIC	44	YEARS	TRT02	DRUG_B	INV06	DR_GREEN	1979-04-05	2023-06-05	2023-11-05	2023-05-22
5	STUDY001	DM	STUDY001-IND-001	001	1001	INDIA	M	ASIAN	NOT_HISPANIC	34	YEARS	TRT01	DRUG_A	INV01	DR_RAO	1990-05-10	2023-01-10	2023-06-10	2023-01-05
6	STUDY001	DM	STUDY001-IND-002	002	1001	INDIA	F	ASIAN	NOT_HISPANIC	29	YEARS	TRT02	DRUG_B	INV01	DR_RAO	1995-08-21	2023-01-12	2023-06-15	2023-01-07
7	STUDY001	DM	STUDY001-JPN-005	005	3001	JAPAN	M	ASIAN	NOT_HISPANIC	52	YEARS	TRT01	DRUG_A	INV03	DR_TANAKA	1971-07-09	2023-03-01	2023-08-01	2023-02-20
8	STUDY001	DM	STUDY001-JPN-006	006	3001	JAPAN	F	ASIAN	NOT_HISPANIC	47	YEARS	TRT02	DRUG_B	INV03	DR_TANAKA	1976-01-18	2023-03-05	2023-08-05	2023-02-22
9	STUDY001	DM	STUDY001-UK-007	007	4001	UK	M	WHITE	NOT_HISPANIC	60	YEARS	TRT01	DRUG_A	INV04	DR_BROWN	1963-09-25	2023-04-01	2023-09-01	2023-03-18
10	STUDY001	DM	STUDY001-UK-008	008	4001	UK	F	WHITE	NOT_HISPANIC	55	YEARS	TRT02	DRUG_B	INV04	DR_BROWN	1968-12-11	2023-04-03	2023-09-05	2023-03-20
11	STUDY001	DM	STUDY001-USA-003	003	2001	USA	M	WHITE	NOT_HISPANIC	45	YEARS	TRT01	DRUG_A	INV02	DR_SMITH	1978-03-14	2023-02-01	2023-07-01	2023-01-25
12	STUDY001	DM	STUDY001-USA-004	004	2001	USA	F	BLACK	OR_AFRICAN_AMERICAN_HISPANIC	38	YEARS	TRT02	DRUG_B	INV02	DR_SMITH	1986-11-02	2023-02-05	2023-07-10	2023-01-28

P. QUICK DM QUALITY CONTROL SUMMARY

proc sql;

    select 

        count(distinct USUBJID) as TOTAL_SUBJECTS,

        min(AGE) as MIN_AGE,

        max(AGE) as MAX_AGE

    from sdtm_dm;

quit;

OUTPUT:

TOTAL_SUBJECTS	MIN_AGE	MAX_AGE
12	29	60

Q. PRINT DM IN REVIEW-FRIENDLY FORMAT

proc print data=sdtm_dm_full label noobs;

    var STUDYID USUBJID SEX AGE AGEU RACE ETHNIC COUNTRY ARM;

run;

OUTPUT:

STUDYID	USUBJID	SEX	AGE	AGEU	RACE	ETHNIC	COUNTRY	ARM
STUDY001	STUDY001-AUS-009	M	41	YEARS	WHITE	NOT_HISPANIC	AUSTRALIA	DRUG_A
STUDY001	STUDY001-AUS-010	F	36	YEARS	WHITE	NOT_HISPANIC	AUSTRALIA	DRUG_B
STUDY001	STUDY001-CAN-011	M	49	YEARS	WHITE	NOT_HISPANIC	CANADA	DRUG_A
STUDY001	STUDY001-CAN-012	F	44	YEARS	WHITE	NOT_HISPANIC	CANADA	DRUG_B
STUDY001	STUDY001-IND-001	M	34	YEARS	ASIAN	NOT_HISPANIC	INDIA	DRUG_A
STUDY001	STUDY001-IND-002	F	29	YEARS	ASIAN	NOT_HISPANIC	INDIA	DRUG_B
STUDY001	STUDY001-JPN-005	M	52	YEARS	ASIAN	NOT_HISPANIC	JAPAN	DRUG_A
STUDY001	STUDY001-JPN-006	F	47	YEARS	ASIAN	NOT_HISPANIC	JAPAN	DRUG_B
STUDY001	STUDY001-UK-007	M	60	YEARS	WHITE	NOT_HISPANIC	UK	DRUG_A
STUDY001	STUDY001-UK-008	F	55	YEARS	WHITE	NOT_HISPANIC	UK	DRUG_B
STUDY001	STUDY001-USA-003	M	45	YEARS	WHITE	NOT_HISPANIC	USA	DRUG_A
STUDY001	STUDY001-USA-004	F	38	YEARS	BLACK	OR_AFRICAN_AMERICAN_HISPANIC	USA	DRUG_B

/*USED TO CHECK SDTM REQUIRED PARAMETERS ARE CONTAINED OR NOT*/

proc cdisc model=sdtm;

 sdtm sdtmversion="3.1";

 domaindata data=work.sdtm_dm

       domain=dm

   category=special;

run;

LOG:

NOTE: DomainData DM data content is valid.

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