title: Extending the classification approach for comparing two active treatment arms to binary and time-to-event outcomes.
abstract: For regulatory purposes, in a trial comparing two active treatments, a hypothesis such as noninferiority or superiority must be prespecified even when there is little known about how they compare against each other or when the objective is simply to identify the best. In this article, we extend an alternative classification methodology, the classification approach of Qu et al. (Statistics in Medicine, 30:3488-3495), to compare two active treatments when outcomes are binary and time-to-event variables. This method based on estimation approach instead of hypothesis testing can be useful when little prior information is available on which treatment has better efficacy. The entire decision space is divided into eight distinct possible outcomes based on predefined lower and upper non-inferiority margins, and the conclusion will be drawn according to the location of the confidence interval for relative risk or hazard ratio (or its logarithm transformation). We demonstrate theoretically that this method controls the misclassification rate at the specified level. We also illustrate the method by simulations and using data from a Phase 3 first-line nonsmall cell lung cancer study.
authors.affiliation: a Department of Global Statistical Science , Eli Lilly and Company , Indianapolis , Indiana , USA.
authors.forename: Jingyi
authors.initials: J
authors.lastname: Liu
authors.affiliation: a Department of Global Statistical Science , Eli Lilly and Company , Indianapolis , Indiana , USA.
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
authors.affiliation: a Department of Global Statistical Science , Eli Lilly and Company , Indianapolis , Indiana , USA.
authors.forename: Pandurang M
authors.initials: PM
authors.lastname: Kulkarni
mesh_terms: D017326:Clinical Trials, Phase III as Topic; D016001:Confidence Intervals; D006801:Humans; D016016:Proportional Hazards Models; D012107:Research Design; D012306:Risk; D016896:Treatment Outcome
keywords: Confidence interval; NSCLC; misclassification rate