Efficacy Evaluation in Acne Clinical Trials

1. Efficacy Evaluation in Acne Clinical Trials Mohamed Alosh, Ph.D. Kathleen Fritsch, Ph.D. Shiowjen Lee, Ph.D. DBIII, OB, CDER, FDA

2. An Outline 1. Re-visit choice of primary endpoints (statistical viewpoint) 2. Statistical analysis methods and data transformations 3. Repeated measurements vs. final assessment 4. Effect of baseline severity 5. Final Comments

3. 1. Primary Endpoints in Acne Trials (Statistical Viewpoint) • Inflammatory, non-inflammatory, and Total Lesion Counts can be analyzed as • final lesion counts • change from baseline • percent change • Investigator Global Evaluation (IGE)

4. Pros Easy to interpret & analyze Attempts to remove influence of baseline counts Cons Baseline may still have influence since change is negatively correlated with final counts Change and percent change scores may have highly skewed distributions (violates parametric tests) Analysis of Change Scores

5. Figure 1. Mean Lesion Counts by Type over Time(Drug X, Study 1)

6. Figure 2. Mean Lesion Counts by Type over Time(Drug Y, Study 2)

7. Figure 3. Inflammatory Counts at Week 12 vs. Baseline Count (Drug X, Vehicle Arm)

8. Figure 4. Inflammatory Counts at Week 12 vs. Baseline Count (Drug X, Active Arm)

9. Figure 5. Mean % Change in Infl. Lesions over Time (Drug X, Study 1)

10. Figure 6. Subject’s Total Lesion Count over Time -- Center A (Drug X, Study 1)

11. 2. Statistical Analysis Issues (a) Analysis units • original • transformed data (ranks, log, etc.) (Pros & Cons) (b) Analysis methods for final study endpoint • Simple comparisons • ANOVA (treatment, center, interaction) • ANCOVA (include baseline count as covariate) (Comparison of results presented in Tables 1a-c & Tables 2a-c)

12. 3. Repeated Measures vs. Final Assessment • Repeated measures may increase power for detecting treatment effect • Must select number of timepoints to be included in repeated measurements model • Repeated Measures Models: MANOVA, GLM, MIXED (Comparison of results presented in Tables 1a-c & Tables 2a-c))

13. Table 1a: Treatment effect p-values for various statistical methods (Drug X, Study 1) *(active, vehicle) (R) = Repeated Measures

14. Table 1b: Treatment effect p-values for various statistical methods (Drug X, Study 1) *(active, vehicle) (R) = Repeated Measures

15. Table 1c: Treatment effect p-values for various statistical methods (Drug X, Study 1) *(active, vehicle) (R) = Repeated Measures

16. Comments on Results for Drug X • results for total are similar to non-infl. (strong corr., highly signif.) • no general pattern for ranks vs. original data • for infl. lesions, % change has smaller p-values than counts or change • for change and % change, ANCOVA has similar results to Week 12 analysis • p-values for rep. meas. in general are larger than final study endpoint analysis (prev. weeks not signif.)

17. Table 2a: Treatment effect p-values for various statistical methods (Drug Y, Study 2) *(active, vehicle) (R) = Repeated Measures

18. Table 2b: Treatment effect p-values for various statistical methods (Drug Y, Study 2) *(active, vehicle) (R) = Repeated Measures

19. Table 2c: Treatment effect p-values for various statistical methods (Drug Y, Study 2) *(active, vehicle) (R) = Repeated Measures

20. Comments on Results for Drug Y • results for total & non-infl. are similar, as for X (but here less signif.) • no general pattern for ranks vs. original data • for infl. lesions, % change has larger p-values than counts or change • for change and % change, ANCOVA has similar results to Cycle 6 analysis • p-values for GLM(R)/ANCOVA(R) in general are smaller than final study endpoint

21. 4. Effect of Baseline Severity • Divide subjects into equal sized groups (e.g., quartiles) based on baseline lesion count Plots of lesion counts by baseline category • Compare efficacy results by baseline category • Tables 3a-b present results for lesion counts • Tables 4a-b present results for IGE

22. Figure 7. Mean Week 12 Lesion Counts by Type over Baseline Category (Drug X, Study 1)

23. Figure 8. Mean Cycle 6 Lesion Counts by Type over Baseline Category (Drug Y, Study 2)

24. Table 3.a. Treatment Effect at Week 12 by Baseline Category (Drug X, Study 1)

25. Table 3.b. Comparison of IGE Success* Rates at Week 12 by Baseline Category (Drug X, Study 1) *Success = None or Minimal Acne

26. Table 4.a. Treatment Effect at Cycle 6 by Baseline Category (Drug Y, Study 2)

27. Table 4.b. Comparison of IGE Success* Rates by Baseline Category (Drug Y, Study 2) *Success = ‘Clear’ or ‘Almost Clear/Mild’

28. Comments about efficacy results by baseline category • For the two drugs considered, no general pattern for results for lesion counts by type, their change, or percent change • Similarly, for the two drugs considered, there is also no general pattern for the IGE • For the range of lesion counts in these studies, efficacy results do not appear to vary by baseline severity

29. 5. Final Comments (a) Analysis of change from baseline, percent change, and final counts with baseline as covariate attempt to account for variability at baseline (b) Percent change data could have extreme outliers when the baseline count is relatively small (e.g., inflammatory lesions)

30. Final Comments (cont’d) (c) Repeated measurements approach attempts to reduce the influence of outliers (flares) by ‘averaging’ over time. The impact of the repeated measures on the p-value depends on whether efficacy reached a plateau at previous time points. (d) For the data sets considered, treatment efficacy did not vary by baseline severity whether one considers analysis of lesion counts or IGE