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Abstract We consider the construction of confidence bands for survival curves under the outcome‐dependent stratified sampling. A main challenge of this design is that data are a biased dependent sample due to stratification and sampling without replacement. Most literature on regression approximates this design by Bernoulli sampling but variance is generally overestimated. Even with this approximation, the limiting distribution of the inverse probability weighted Kaplan–Meier estimator involves a general Gaussian process, and hence quantiles of its supremum is not analytically available. In this paper, we provide a rigorous asymptotic theory for the weighted Kaplan–Meier estimator accounting for dependence in the sample. We propose the novel hybrid method to both simulate and bootstrap parts of the limiting process to compute confidence bands with asymptotically correct coverage probability. Simulation study indicates that the proposed bands are appropriate for practical use. A Wilms tumor example is presented. 

Abstract Detection of prognostic factors associated with patients’ survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers. However, new challenges arise from heterogeneity in study populations and designs when jointly analyzing the multiple studies. For example, patients from different cohorts show different demographic characteristics and risk profiles. Existing high-dimensional variable selection methods for survival analysis, however, are restricted to single study analysis. We propose a novel Cox model based two-stage variable selection method called “Cox-TOTEM” to detect survival-associated biomarkers common in multiple genomic studies. Simulations showed our method greatly improved the sensitivity of variable selection as compared to the separate applications of existing methods to each study, especially when the signals are weak or when the studies are heterogeneous. An application of our method to TCGA transcriptomic data identified essential survival associated genes related to the common disease mechanism of five Pan-Gynecologic cancers.