Efficacy Evaluation in Acne Clinical Trials

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

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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)

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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)

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Figure 1. Mean Lesion Counts by Type over
Time(Drug X, Study 1)
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Figure 2. Mean Lesion Counts by Type over
Time(Drug Y, Study 2)
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Figure 3. Inflammatory Counts at Week 12 vs.
Baseline Count (Drug X, Vehicle Arm)
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Figure 4. Inflammatory Counts at Week 12 vs.
Baseline Count (Drug X, Active Arm)
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Figure 5. Mean Change in Infl. Lesions over
Time (Drug X, Study 1)
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Figure 6. Subjects Total Lesion Count over Time
-- Center A (Drug X, Study 1)
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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)

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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))

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

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Table 2a Treatment effect p-values for various
statistical methods (Drug Y, Study 2)
(active, vehicle) (R) Repeated Measures
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Table 2b Treatment effect p-values for various
statistical methods (Drug Y, Study 2)
(active, vehicle) (R) Repeated Measures
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Table 2c Treatment effect p-values for various
statistical methods (Drug Y, Study 2)
(active, vehicle) (R) Repeated Measures
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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

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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

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Figure 7. Mean Week 12 Lesion Counts by Type over
Baseline Category (Drug X, Study 1)
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Figure 8. Mean Cycle 6 Lesion Counts by Type over
Baseline Category (Drug Y, Study 2)
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Table 3.a. Treatment Effect at Week 12 by
Baseline Category (Drug X, Study 1)
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Table 3.b. Comparison of IGE Success Rates at
Week 12 by Baseline Category (Drug X, Study 1)
Success None or Minimal Acne
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Table 4.a. Treatment Effect at Cycle 6 by
Baseline Category (Drug Y, Study 2)
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Table 4.b. Comparison of IGE Success Rates by
Baseline Category (Drug Y, Study 2)
Success Clear or Almost Clear/Mild
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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

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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)

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Final Comments (contd)

• (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