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BLA 1251970 Sipuleucel-T (APC-8015) FDA Statistical Review and Findings Bo-Guang Zhen, PhD Statistical Reviewer, OBE, C PowerPoint Presentation
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BLA 1251970 Sipuleucel-T (APC-8015) FDA Statistical Review and Findings Bo-Guang Zhen, PhD Statistical Reviewer, OBE, CBER March 29, 2007 Cellular, Tissue and Gene Therapies Advisory Committee Meeting. Outline of Presentation. Review of Efficacy Results Issues in Survival Analysis

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slide1

BLA 1251970

Sipuleucel-T (APC-8015)

FDA Statistical Review and Findings

Bo-Guang Zhen, PhD

Statistical Reviewer, OBE, CBER

March 29, 2007

Cellular, Tissue and Gene Therapies Advisory Committee Meeting

slide2

Outline of Presentation

  • Review of Efficacy Results
  • Issues in Survival Analysis
  • Limitations of Post-Hoc Analysis
  • Challenges in Survival Analysis
slide3

Review of Efficacy Results

  • Two Phase III studies as main efficacy evidence to support licensing application (Studies 1 and 2)
  • Both studies failed to
    • meet the primary endpoint of time to disease progression (TTP)
    • demonstrate statistical significance for other pre-specified endpoints
  • Key efficacy evidence was based on the difference in overall survival (OS) between two arms
slide4

Review of Overall Survival Analysis

* CI = confidence interval, unit for survival is in month

slide5

Survival Sensitivity Analysis for Study 1 Covariate Adjustment in Cox model (N=127)

* The sponsor’s analysis ~ HR = Hazard ratio

slide6

Survival Sensitivity Analysis for Study 1 Covariate Adjustment in Cox model (Cont.)

Impact of missing covariate data in Cox Model (I):

slide7

Survival Sensitivity Analysis for Study 1 Covariate Adjustment in Cox model (Cont.)

  • Exclusion of patients due to missing covariate data could lead to biased estimates
  • Although p-values for treatment effect were greater than 0.05 in a few sensitivity analyses, the majority of the sensitivity analyses resulted in a p-value of <0.05
  • Sensitivity analyses supported the “statistically significant finding” for OS
slide8

Survival Analysis for Study 2

  • p= 0.331 based on log-rank test
  • Some patients may be excluded in sensitivity analyses using Cox model which could lead to biased estimates
  • Hypothesis test for treatment effect in Cox model resulted in:

o p-values from 0.023 to 0.642

o p > 0.05 in most analyses

  • Sensitivity analyses did not support the “statistically significant finding” for OS
slide9
Review of Overall Survival Analysis--- Sensitivity analyses support the “statistically significant finding” for Study 1
slide10

Issues in Survival Analysis

  • Overall survival (OS) as an endpoint was not defined in either study protocols
  • A statistical analysis method for the primary comparison in OS was not pre-specified
  • The alpha level (probability of making a false positive claim for treatment effect) was not allocated to the primary test for OS
  • The ‘post-hoc’ analyses make it difficult to interpret the hypothesis test results for OS
slide11

Limitations of Post-Hoc AnalysisPre-specified vs. post-hoc analysis

For designing a confirmative trial, it is essential to:

  • Define endpoint(s) clearly
  • Describe statistical analysis method(s) and state which one would be used for the primary comparison
  • Set alpha level. e.g.: α = 0.05
  • Allocate alpha level to each test if multiplicity adjustment is needed

-- Then, one is able to say:

the difference is statistically significant or not based on the p-value from the primary comparison.

Otherwise, it is difficult to interpret the p-value

slide12

Hypothetical cases: Interpretation of p-value in studies with pre-specified analysis

* NS: non-statistically significant at the level of 0.05

slide13

Limitations of Post-Hoc Analysis-- Difficulty in interpreting p-value

  • Obtaining a p-value of 0.01 (or < 0.05) may not always be considered statistically significant in a well pre-specified analysis.
  • When a study fails to meet its primary endpoint(s), there is no alpha left for other endpoint analyses. So literally, the difference in other endpoints should not be considered statistically significant.
  • Therefore, it is difficult to interpret the hypothesis test result for OS in Study 1 (p=0.01)
slide14

Limitations of Post-Hoc Analysis

  • In post-hoc analyses one could

o keep conducting hypothesis tests for treatment effect on different endpoints and/or on the same endpoint using different analysis method

o then easily obtain a so called “statistically significant result” (p< 0.05) even when there is no treatment effect

  • If OS is one of the many un-specified endpoints under the testing, it is possible that a p-value of 0.01 was observed just by chance
  • However, OS is a preferred endpoint for cancer trial
challenges in survival analysis
Challenges in Survival Analysis
  • Difficulty in interpreting the p-value (0.01)
  • “Statistical significance” only demonstrated in Study 1.
  • The lower bound of 95% CI for hazard ratio (1.13) close to one