New Tests for Assessing Non-Inferiority and Equivalence from Survival Data

Kallappa M. Koti

United States Food and Drug Administration, Silver Spring, USA.

DOI: 10.4236/ojs.2013.32008                

Abstract

We propose a new nonparametric method for assessing non-inferiority of an experimental therapy compared to a standard of care. The ratio μ_E/μ_R of true median survival times is the parameter of interest. This is of considerable interest in clinical trials of generic drugs. We think of the ratio m_E/m_R of the sample medians as a point estimate of the ratioμ_E/μ_R. We use the Fieller-Hinkley distribution of the ratio of two normally distributed random variables to derive an unbiased level-α test of inferiority null hypothesis, which is stated in terms of the ratio μ_E/μ_R and a pre-specified fixed non-inferiority margin δ. We also explain how to assess equivalence and non-inferiority using bootstrap equivalent confidence intervals on the ratioμ_E/μ_R. The proposed new test does not require the censoring distributions for the two arms to be equal and it does not require the hazard rates to be proportional. If the proportional hazards assumption holds good, the proposed new test is more attractive. We also discuss sample size determination. We claim that our test procedure is simple and attains adequate power for moderate sample sizes. We extend the proposed test procedure to stratified analysis. We propose a “two one-sided tests” approach for assessing equivalence.

Keywords

Right-Censored Data; Kaplan-Meier Estimate; Bootstrap Standard Error; Generic Drugs

Conflicts of Interest

The authors declare no conflicts of interest.

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