Covariate-adjusted Group Sequential Comparisons of Survival Probabilities
arxiv(2024)
摘要
In confirmatory clinical trials, survival outcomes are frequently studied and
interim analyses for efficacy and/or futility are often desirable. Methods such
as the log rank test and Cox regression model are commonly used to compare
treatments in this setting. They rely on a proportional hazards (PH) assumption
and are subject to type I error rate inflation and loss of power when PH are
violated. Such violations may be expected a priori, particularly when the
mechanisms of treatments differ such as immunotherapy vs. chemotherapy for
treating cancer. We develop group sequential tests for comparing survival
curves with covariate adjustment that allow for interim analyses in the
presence of non-PH and offer easily interpreted, clinically meaningful summary
measures of the treatment effect. The joint distribution of repeatedly computed
test statistics converges to the canonical joint distribution with a Markov
structure. The asymptotic distribution of the test statistics allows marginal
comparisons of survival probabilities at multiple fixed time points and
facilitates both critical value specification to maintain type I error control
and sample size/power determination. Simulations demonstrate that the achieved
type I error rate and power of the proposed tests meet targeted levels and are
robust to the PH assumption and covariate influence. The proposed tests are
illustrated using a clinical trial dataset from the Blood and Marrow Transplant
Clinical Trials Network 1101 trial.
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