  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.

Methodology;Applications;Machine Learning