# Presentation: 341063

## Efficacy Evaluation in Acne Clinical Trials

## 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

## 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)

## Analysis of Change Scores

**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)

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

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

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

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

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

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

## 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)

## 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))

## Table 1a: Treatment effect p-values for various statistical methods (Drug X, Study 1)

- *(active, vehicle)		(R) = Repeated Measures

## Table 1b: Treatment effect p-values for various statistical methods (Drug X, Study 1)

- *(active, vehicle)		(R) = Repeated Measures

## Table 1c:  Treatment effect p-values for various statistical methods (Drug X, Study 1)

- *(active, vehicle)		(R) = Repeated Measures

## 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.)

## Table 2a: Treatment effect p-values for various statistical methods (Drug Y, Study 2)

- *(active, vehicle)		(R) = Repeated Measures

## Table 2b: Treatment effect p-values for various statistical methods (Drug Y, Study 2)

- *(active, vehicle)		(R) = Repeated Measures

## Table 2c:  Treatment effect p-values for various statistical methods (Drug Y, Study 2)

- *(active, vehicle)		(R) = Repeated Measures

## 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

## 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

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

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

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

## *Success = None or Minimal Acne

- Table 3.b.  Comparison of IGE Success* Rates at Week 12 by Baseline Category (Drug X, Study 1)

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

## *Success = ‘Clear’ or ‘Almost Clear/Mild’

- Table 4.b.  Comparison of IGE Success* Rates by Baseline Category (Drug Y, Study 2)

## 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

## 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)

## 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