1 / 15

BLA 1251970 Sipuleucel-T (APC-8015) FDA Statistical Review and Findings Bo-Guang Zhen, PhD Statistical Reviewer, OBE, C

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

sibley
Download Presentation

BLA 1251970 Sipuleucel-T (APC-8015) FDA Statistical Review and Findings Bo-Guang Zhen, PhD Statistical Reviewer, OBE, C

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Outline of Presentation • Review of Efficacy Results • Issues in Survival Analysis • Limitations of Post-Hoc Analysis • Challenges in Survival Analysis

  3. 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

  4. Review of Overall Survival Analysis * CI = confidence interval, unit for survival is in month

  5. Survival Sensitivity Analysis for Study 1 Covariate Adjustment in Cox model (N=127) * The sponsor’s analysis ~ HR = Hazard ratio

  6. Survival Sensitivity Analysis for Study 1 Covariate Adjustment in Cox model (Cont.) Impact of missing covariate data in Cox Model (I):

  7. 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

  8. 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

  9. Review of Overall Survival Analysis--- Sensitivity analyses support the “statistically significant finding” for Study 1

  10. 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

  11. 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

  12. Hypothetical cases: Interpretation of p-value in studies with pre-specified analysis * NS: non-statistically significant at the level of 0.05

  13. 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)

  14. 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

  15. 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

More Related