TITLE:
New Tests for Assessing Non-Inferiority and Equivalence from Survival Data
AUTHORS:
Kallappa M. Koti
KEYWORDS:
Right-Censored Data; Kaplan-Meier Estimate; Bootstrap Standard Error; Generic Drugs
JOURNAL NAME:
Open Journal of Statistics,
Vol.3 No.2,
April
16,
2013
ABSTRACT: We propose a new nonparametric method for
assessing non-inferiority of an experimental therapy compared to a standard of
care. The ratio μE/μR 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 mE/mR of the sample medians as a point estimate of the ratioμE/μR. 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 μE/μR 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
ratioμE/μR. 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.