Randomized clinical trials are commonly designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, weakens 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 (Step 1). At the analysis stage, using observed survival times but blinded to patient-level treatment assignment, the covariate list is shortened via elastic net Cox regression (Step 2) for input into a conditional inference tree algorithm that segments the heterogeneous trial population into subpopulations (strata) of prognostically homogeneous patients (Step 3). After patient-level treatment unblinding, a treatment comparison is done within each formed stratum (Step 4) and stratum-level results are combined for statistical inference using an adaptive strategy (Step 5). The impressive power-boosting performance of our proposed 5-step stratified testing and amalgamation routine (5-STAR) relative to the logrank test and other methods for survival analysis is illustrated using two real datasets and simulations. An R package is available for implementation.
The Workshop is held from 1:30-2:50pm in Medical School Office Building (MSOB), Rm x303, 1265 Welch Road, Stanford, unless otherwise specified on the calendar at the link below.