Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

TL;DR

This paper develops simultaneous inference procedures for multiple characteristics of survival functions, addressing violations of proportional hazards and providing methods for more comprehensive treatment effect assessment.

Contribution

It introduces multivariate normal approximation-based multiple testing procedures for various survival parameters, including milestone survival, quantiles, and mean survival times, with software implementation.

Findings

Methods control type I error in simulations

Procedures demonstrate good power in finite samples

Application shown with oncology trial data

Abstract

Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a-priori violated, e.g. due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials the according inferential procedures need to be adjusted for multiplicity. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we propose according parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.