Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical Trials
This addresses the challenge of regulatory approval for safe and effective therapies in randomized clinical trials by improving statistical power in heterogeneous populations, though it is incremental as it builds on existing methods like elastic net and conditional inference trees.
The paper tackles the problem of detecting treatment differences in survival analysis for heterogeneous clinical trial populations by proposing a 5-step stratified testing and amalgamation routine (5-STAR), which boosts power relative to common approaches like the logrank test, as demonstrated with hypothetical, real datasets, and simulations.
Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution to this problem is proposed. A list of baseline covariates that have the potential to be prognostic for survival under either treatment is pre-specified in the analysis plan. At the analysis stage, using all observed survival times but blinded to patient-level treatment assignment, 'noise' covariates are removed with elastic net Cox regression. The shortened covariate list is used by a conditional inference tree algorithm to segment the heterogeneous trial population into subpopulations of prognostically homogeneous patients (risk strata). After patient-level treatment unblinding, a treatment comparison is done within each formed risk stratum and stratum-level results are combined for overall statistical inference. The impressive power-boosting performance of our proposed 5-step stratified testing and amalgamation routine (5-STAR), relative to that of the logrank test and other common approaches that do not leverage inherently structured patient heterogeneity, is illustrated using a hypothetical and two real datasets along with simulation results. Furthermore, the importance of reporting stratum-level comparative treatment effects (time ratios from accelerated failure time model fits in conjunction with model averaging and, as needed, hazard ratios from Cox proportional hazard model fits) is highlighted as a potential enabler of personalized medicine. A fiveSTAR R package is available at https://github.com/rmarceauwest/fiveSTAR.