Exact test and exact confidence interval for the Cox model

The Cox proportional hazards model is commonly used to analyze time-to-event data in clinical trials. Standard inference procedures for the Cox model are based on asymptotic approximations and may perform poorly when there are few events in one or both treatment groups, as may be the case when the event of interest is rare or when the experimental treatment is highly efficacious. In this article, we propose an exact test of equivalence and efficacy under a proportional hazard model with treatment effect as the only fixed effect, together with an exact confidence interval that is obtained by inverting the exact test. The proposed test is based on a conditional error method originally proposed for sample size reestimation problems. In the present context, the conditional error method is used to combine information from a sequence of hypergeometric distributions, one at each observed event time. The proposed procedures are evaluated in simulation studies and illustrated using real data from an HIV prevention trial. A companion R package "ExactCox" is available for download on CRAN.