182.CREATING A UNIQUE CYBER-THERAPY ADaM DATASET WITH MORE THAN 15 OBSERVATIONS USING PROC SQL | PROC PRINT | PROC MEANS | PROC SORT | PROC FORMAT | SAS MACROS IN A STEP-BY-STEP APPROACH FOR CLINICAL TRIAL ANALYSIS

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CREATING A UNIQUE CYBER-THERAPY ADaM DATASET WITH MORE THAN 15 OBSERVATIONS USING PROC SQL | PROC PRINT | PROC MEANS | PROC SORT | PROC FORMAT | SAS MACROS IN A  STEP-BY-STEP APPROACH FOR CLINICAL TRIAL ANALYSIS


/*Creating an ADaM (Analysis Data Model) dataset from scratch using SQL and macros in SAS*/

/*A clinical trial studying the effect of a cyber-cognitive therapy on digital stress*/


Step 1: Study Design Overview

Therapy Goal: Reduce digital stress using cyber-cognitive training.

Key Parameters:

Study ID: CYBER001

Arms: Control (Placebo) and Treatment (Cognitive App)

Duration: 10 days

Subjects: 18 unique participants

Endpoint: Reduction in daily anxiety score (scale: 0–10)


Step 2: Simulating Raw SDTM-Like Data

Simulating data for the SDTM domains:

DM (Demographics)

QS (Questionnaires - daily stress score)


2.1 Create DM (Demographics) Domain

data sdm_dm;

    input STUDYID $ USUBJID $ AGE SEX $ ARMCD $;

    datalines;

CYBER001 SUBJ001 25 M TRT

CYBER001 SUBJ002 33 F TRT

CYBER001 SUBJ003 29 M TRT

CYBER001 SUBJ004 41 F TRT

CYBER001 SUBJ005 35 M TRT

CYBER001 SUBJ006 27 F TRT

CYBER001 SUBJ007 38 M TRT

CYBER001 SUBJ008 31 F TRT

CYBER001 SUBJ009 40 M TRT

CYBER001 SUBJ010 24 F TRT

CYBER001 SUBJ011 26 M PLC

CYBER001 SUBJ012 39 F PLC

CYBER001 SUBJ013 37 M PLC

CYBER001 SUBJ014 28 F PLC

CYBER001 SUBJ015 36 M PLC

CYBER001 SUBJ016 30 F PLC

CYBER001 SUBJ017 42 M PLC

CYBER001 SUBJ018 34 F PLC

;

run;

proc print;run;

Output:

Obs STUDYID USUBJID AGE SEX ARMCD
1 CYBER001 SUBJ001 25 M TRT
2 CYBER001 SUBJ002 33 F TRT
3 CYBER001 SUBJ003 29 M TRT
4 CYBER001 SUBJ004 41 F TRT
5 CYBER001 SUBJ005 35 M TRT
6 CYBER001 SUBJ006 27 F TRT
7 CYBER001 SUBJ007 38 M TRT
8 CYBER001 SUBJ008 31 F TRT
9 CYBER001 SUBJ009 40 M TRT
10 CYBER001 SUBJ010 24 F TRT
11 CYBER001 SUBJ011 26 M PLC
12 CYBER001 SUBJ012 39 F PLC
13 CYBER001 SUBJ013 37 M PLC
14 CYBER001 SUBJ014 28 F PLC
15 CYBER001 SUBJ015 36 M PLC
16 CYBER001 SUBJ016 30 F PLC
17 CYBER001 SUBJ017 42 M PLC
18 CYBER001 SUBJ018 34 F PLC


2.2 Create QS (Daily Anxiety Scores)

Use a macro to simulate 10 days of anxiety scores per subject.

%macro generate_qs_data;

data sdm_qs;

    length STUDYID USUBJID $10 QSTESTCD $10 QSTEST $50 VISIT $10;

    format QSDTC date9.;

    retain QSTESTCD 'DSTRS' QSTEST 'Daily Stress Score';

    do subj = 1 to 18;

        usubjid = cats('SUBJ', put(subj, z3.));

        studyid = 'CYBER001';

        do day = 1 to 10;

            visit = cats("Day", day);

            qsdtc = '01JAN2024'd + day - 1;

            score = ceil(ranuni(1234) * 10);  /* Random score 1–10 */

            output;

        end;

    end;

run;

proc print;run;

%mend;


%generate_qs_data

Output:

Obs STUDYID USUBJID QSTESTCD QSTEST VISIT QSDTC subj day score
1 CYBER001 SUBJ001 DSTRS Daily Stress Score Day1 01JAN2024 1 1 3
2 CYBER001 SUBJ001 DSTRS Daily Stress Score Day2 02JAN2024 1 2 1
3 CYBER001 SUBJ001 DSTRS Daily Stress Score Day3 03JAN2024 1 3 4
4 CYBER001 SUBJ001 DSTRS Daily Stress Score Day4 04JAN2024 1 4 1
5 CYBER001 SUBJ001 DSTRS Daily Stress Score Day5 05JAN2024 1 5 3
6 CYBER001 SUBJ001 DSTRS Daily Stress Score Day6 06JAN2024 1 6 1
7 CYBER001 SUBJ001 DSTRS Daily Stress Score Day7 07JAN2024 1 7 1
8 CYBER001 SUBJ001 DSTRS Daily Stress Score Day8 08JAN2024 1 8 2
9 CYBER001 SUBJ001 DSTRS Daily Stress Score Day9 09JAN2024 1 9 5
10 CYBER001 SUBJ001 DSTRS Daily Stress Score Day10 10JAN2024 1 10 2
11 CYBER001 SUBJ002 DSTRS Daily Stress Score Day1 01JAN2024 2 1 1
12 CYBER001 SUBJ002 DSTRS Daily Stress Score Day2 02JAN2024 2 2 5
13 CYBER001 SUBJ002 DSTRS Daily Stress Score Day3 03JAN2024 2 3 1
14 CYBER001 SUBJ002 DSTRS Daily Stress Score Day4 04JAN2024 2 4 10
15 CYBER001 SUBJ002 DSTRS Daily Stress Score Day5 05JAN2024 2 5 10
16 CYBER001 SUBJ002 DSTRS Daily Stress Score Day6 06JAN2024 2 6 8
17 CYBER001 SUBJ002 DSTRS Daily Stress Score Day7 07JAN2024 2 7 5
18 CYBER001 SUBJ002 DSTRS Daily Stress Score Day8 08JAN2024 2 8 4
19 CYBER001 SUBJ002 DSTRS Daily Stress Score Day9 09JAN2024 2 9 4
20 CYBER001 SUBJ002 DSTRS Daily Stress Score Day10 10JAN2024 2 10 6

Step 3: Define ADaM Dataset Structure (ADQS)

Derive ADQS, where:

Each record is a subject-day combination

Include:

PARAMCD: DSTRS

AVAL: Actual score

CHG: Change from baseline

BASE: Day 1 score

TRTFL: Treatment flag (Y/N)


Step 4: Derive ADaM Variables Using SQL & Macros

4.1 Combine DM and QS

proc sql;

    create table adqs_base as

    select 

        q.studyid,

        q.usubjid,

        d.armcd,

        d.age,

        d.sex,

        q.qstestcd as paramcd,

        q.qstest as param,

        q.visit,

        q.qsdtc,

        q.score as aval

    from sdm_qs q

    inner join sdm_dm d

    on q.usubjid = d.usubjid;

quit;

proc print data=adqs_base;

run;

Output:

Obs STUDYID USUBJID ARMCD AGE SEX paramcd param VISIT QSDTC aval
1 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day1 01JAN2024 3
2 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day2 02JAN2024 1
3 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day3 03JAN2024 4
4 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day4 04JAN2024 1
5 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day5 05JAN2024 3
6 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day6 06JAN2024 1
7 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day7 07JAN2024 1
8 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day8 08JAN2024 2
9 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day9 09JAN2024 5
10 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day10 10JAN2024 2
11 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day1 01JAN2024 1
12 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day2 02JAN2024 5
13 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day3 03JAN2024 1
14 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day4 04JAN2024 10
15 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day5 05JAN2024 10
16 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day6 06JAN2024 8
17 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day7 07JAN2024 5
18 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day8 08JAN2024 4
19 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day9 09JAN2024 4
20 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day10 10JAN2024 6


4.2 Identify Baseline (Day1)

proc sql;

    create table baseline as

    select usubjid, aval as base

    from adqs_base

    where visit = "Day1";

quit;

proc print data=baseline;

run;

Output:

Obs USUBJID base
1 SUBJ001 3
2 SUBJ002 1
3 SUBJ003 3
4 SUBJ004 8
5 SUBJ005 5
6 SUBJ006 4
7 SUBJ007 7
8 SUBJ008 9
9 SUBJ009 3
10 SUBJ010 9
11 SUBJ011 1
12 SUBJ012 3
13 SUBJ013 5
14 SUBJ014 8
15 SUBJ015 6
16 SUBJ016 8
17 SUBJ017 3
18 SUBJ018 5


4.3 Merge Baseline and Calculate Change from Baseline

proc sql;

    create table adqs_step2 as

    select a.*, b.base, (a.aval - b.base) as chg

    from adqs_base a

    left join baseline b

    on a.usubjid = b.usubjid;

quit;

proc print data=adqs_step2;

run;

Output:

Obs STUDYID USUBJID ARMCD AGE SEX paramcd param VISIT QSDTC aval base chg
1 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day4 04JAN2024 1 3 -2
2 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day2 02JAN2024 1 3 -2
3 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day6 06JAN2024 1 3 -2
4 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day1 01JAN2024 3 3 0
5 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day5 05JAN2024 3 3 0
6 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day3 03JAN2024 4 3 1
7 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day8 08JAN2024 2 3 -1
8 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day9 09JAN2024 5 3 2
9 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day10 10JAN2024 2 3 -1
10 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day7 07JAN2024 1 3 -2
11 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day5 05JAN2024 10 1 9
12 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day4 04JAN2024 10 1 9
13 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day2 02JAN2024 5 1 4
14 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day6 06JAN2024 8 1 7
15 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day10 10JAN2024 6 1 5
16 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day1 01JAN2024 1 1 0
17 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day8 08JAN2024 4 1 3
18 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day3 03JAN2024 1 1 0
19 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day7 07JAN2024 5 1 4
20 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day9 09JAN2024 4 1 3


Step 5: Add Derived Variables Using Macro

Use a macro to add:

TRTFL: Treatment flag (Y/N)

AVISITN: Numeric visit

ANL01FL: Analysis flag for primary endpoint (Day10)


%macro derive_adqs;

data adqs_final;

    set adqs_step2;

    length trtfl $1 anl01fl $1;

    if armcd = 'TRT' then trtfl = 'Y';

    else trtfl = 'N';

    avisitn = input(compress(visit, ,'kd'), best.);

    if visit = 'Day10' then anl01fl = 'Y';

    else anl01fl = '';

run;

proc print;run;

%mend;


%derive_adqs

Output

Obs STUDYID USUBJID ARMCD AGE SEX paramcd param VISIT QSDTC aval base chg trtfl anl01fl avisitn
1 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day4 04JAN2024 1 3 -2 Y   4
2 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day2 02JAN2024 1 3 -2 Y   2
3 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day6 06JAN2024 1 3 -2 Y   6
4 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day1 01JAN2024 3 3 0 Y   1
5 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day5 05JAN2024 3 3 0 Y   5
6 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day3 03JAN2024 4 3 1 Y   3
7 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day8 08JAN2024 2 3 -1 Y   8
8 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day9 09JAN2024 5 3 2 Y   9
9 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day10 10JAN2024 2 3 -1 Y Y 10
10 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day7 07JAN2024 1 3 -2 Y   7
11 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day5 05JAN2024 10 1 9 Y   5
12 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day4 04JAN2024 10 1 9 Y   4
13 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day2 02JAN2024 5 1 4 Y   2
14 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day6 06JAN2024 8 1 7 Y   6
15 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day10 10JAN2024 6 1 5 Y Y 10
16 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day1 01JAN2024 1 1 0 Y   1
17 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day8 08JAN2024 4 1 3 Y   8
18 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day3 03JAN2024 1 1 0 Y   3
19 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day7 07JAN2024 5 1 4 Y   7
20 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day9 09JAN2024 4 1 3 Y   9


 Final Dataset: ADQS

 proc print data=adqs_final(obs=20);  /* Display first 20 records */

    title "Final ADQS Dataset - First 20 Observations";

run;

Output:

                                                                       Final ADQS Dataset - First 20 Observations

Obs STUDYID USUBJID ARMCD AGE SEX paramcd param VISIT QSDTC aval base chg trtfl anl01fl avisitn
1 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day4 04JAN2024 1 3 -2 Y   4
2 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day2 02JAN2024 1 3 -2 Y   2
3 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day6 06JAN2024 1 3 -2 Y   6
4 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day1 01JAN2024 3 3 0 Y   1
5 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day5 05JAN2024 3 3 0 Y   5
6 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day3 03JAN2024 4 3 1 Y   3
7 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day8 08JAN2024 2 3 -1 Y   8
8 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day9 09JAN2024 5 3 2 Y   9
9 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day10 10JAN2024 2 3 -1 Y Y 10
10 CYBER001 SUBJ001 TRT 25 M DSTRS Daily Stress Score Day7 07JAN2024 1 3 -2 Y   7
11 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day5 05JAN2024 10 1 9 Y   5
12 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day4 04JAN2024 10 1 9 Y   4
13 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day2 02JAN2024 5 1 4 Y   2
14 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day6 06JAN2024 8 1 7 Y   6
15 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day10 10JAN2024 6 1 5 Y Y 10
16 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day1 01JAN2024 1 1 0 Y   1
17 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day8 08JAN2024 4 1 3 Y   8
18 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day3 03JAN2024 1 1 0 Y   3
19 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day7 07JAN2024 5 1 4 Y   7
20 CYBER001 SUBJ002 TRT 33 F DSTRS Daily Stress Score Day9 09JAN2024 4 1 3 Y   9

Step 6: Validate and Summarize

Generate a summary report:

proc sql;

    select armcd, visit, 

           count(*) as n, 

           mean(aval) as mean_score,

           mean(chg) as mean_change

    from adqs_final

    group by armcd, visit;

quit;

Output:

ARMCD VISIT n mean_score mean_change
PLC Day1 8 4.875 0
PLC Day10 8 6.25 1.375
PLC Day2 8 4.75 -0.125
PLC Day3 8 6.25 1.375
PLC Day4 8 6 1.125
PLC Day5 8 7.625 2.75
PLC Day6 8 7 2.125
PLC Day7 8 6.25 1.375
PLC Day8 8 6 1.125
PLC Day9 8 3.375 -1.5
TRT Day1 10 5.2 0
TRT Day10 10 5.7 0.5
TRT Day2 10 5.4 0.2
TRT Day3 10 3.4 -1.8
TRT Day4 10 6.7 1.5
TRT Day5 10 5.1 -0.1
TRT Day6 10 6.1 0.9
TRT Day7 10 3.9 -1.3
TRT Day8 10 5.3 0.1
TRT Day9 10 6.2 1

Summary

Step Description

1         Defined a unique clinical study focused on digital stress

2         Simulated SDTM domains (DM, QS) for 18 subjects

3         Designed ADQS ADaM dataset with key derived variables

4         Used SQL joins and calculations to derive BASE, CHG

5            Applied SAS Macros to add flags and visit numbers

6            Validated with summary stats


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