Utilize Dummy Datasets in Clinical Statistical Programming

1. Utilize Dummy Datasets in Clinical Statistical Programming Amos Shu

2. Introduction • Tables such as PE, Demo, and some Efficacy, etc. need to be partially made up, e.g. data collectability • Dummy datasets – an effective way to improve programming efficiency on these situations • Six ways to utilize dummy datasets • Make up PE tables • Make up Demographic Char. Tables • Optimize datasets in efficacy analysis • Stack datasets in macro • Impute LOCF • Add extra lines in RTF output

3. 1. MAKE UP PE (Physical Examination) TABLES • Each organ should have all medical scenarios - ‘Normal’, ‘Abnormal’, and ‘Not Done’ displayed Placebo Treat1 Treat2 (N=45) (N=43) (N=43) ___________________________________________ Heart Normal 8 9 11 Abnormal 0 0 0 Not Done 0 0 0 Lungs/Chest Normal 7 8 10 Abnormal 0 1 0 Not Done 1 0 1 ...... ____________________________________________

4. 1. MAKE UP PE TABLES (cont.) • Not all subjects have all findings for each organ in real practice • Create a dummy dataset: DATA dummy; DO x= 1 to 9; DO y= 1 to 3; Output; END; END; RUN; X – organ y – finding

5. 1. MAKE UP PE TABLES (cont.)

6. 2. MAKE UP DEMOGRAPHIC CHAR. TABLES • Most common issue - not all races are available in real data Placebo Treat1 Treat2 (N=45) (N=43) (N=43) ____________________________________________________________ Race Caucasian 28 29 27 African American 0 3 1 Asian 10 11 9 American Indian or Alaska native 0 0 0 Native Hawaiian or Pacific Islander 0 0 0 Other 1 0 1 ...... ____________________________________________________________

7. 3. OPTIMIZE DATASETS IN EFFICACY ANALYSIS • Various subgroup efficacy analysis – handle using one macro • Geographic subgroups – US, Europe, and Asia • Sex subgroups – Male and Female • Race subgroups – Asian and Non-Asian • Smoking status subgroups – Never smoker and Current smoker • Age subgroups – Age <=65 and age >65 • Etc. • Some subgroup may not have all treatment values, e.g. • Subgroup A – treatment 1 and 2, not 3 • Subgroup B – treatment 1 and 3, not 2

8. 3. OPTIMIZE DATASETS IN EFFICACY ANALYSIS (cont.) • Treatment dummy dataset - make the macro work DATA dummytrt; treat1 = . ; treat2 = . ; treat3 = . ; RUN; DATA SubEff_d; MERGE dummytrtSubEff; RUN; * Dummytrt must be the left

9. 4. STACK DATASETS IN MACRO • The number of subgroup datasets are unknown • It is impossible to use a single SET statement to stack all subgroup datasets in a DO loop macro • Need a dummy dataset to avoid macro failure DATA comb ; x = 0 ; RUN;

10. 4. STACK DATASETS IN MACRO (cont.) %DO i= 1 %TO &sqlobs.; ... DATA sub&i. ; ... ; RUN; DATA comb ; SET comb sub&i. ; RUN ; %END; DATA final; SET comb; IF x = 0 THEN DELETE ; RUN;

11. 5. IMPUTE LOCF (Last Observation Carried Forward) • Two types of LOCF: • No data for a visit number • Both the visit number and data are missing

12. 5. IMPUTE LOCF (Last Observation Carried Forward) (cont.) DATA dummy ; SET lockbase; DO AVISITN = 9, 10, 11 ; output ; END; RUN; DATA lockdata2 ; MERGE dummylockdata ; BY USUBJID AVISITN ; RUN;

13. 6. ADD EXTRA LINES IN RTF OUTPUT • Improve the appearance of a table

14. 6. ADD EXTRA LINES IN RTF OUTPUT (cont.) Placebo Treat1 Treat2 (N=45) (N=43) (N=43) ________________________________________________________ Week 1 N 103 103 101 Mean 36.2 37.4 36.6 SD 10.25 10.43 9.77 Median 36.0 37.0 36.0 Minimum 2 11 6 Maximum 59 64 60 Week 2 N 103 102 101 Mean 35.9 37.1 36.3 SD 10.15 10.03 10.07 Median 36.0 37.0 36.0 Minimum 3 10 6 Maximum 58 64 59 ...... __________________________________________________________

15. CONCLUSION • Creating a dummy dataset is easy and simple • It’s extremely helpful to programming • It’s crucial to understand when and how to create a dummy dataset

16. Thank you! Amos Shu Endo Pharmaceuticals