Searcharxiv⌕ Search

arXiv subjects

Michael J. Martens

Publications and source records attributed to Michael J. Martens.

2 recordsLinked to original sources

TITE-Safety: Time-to-event Safety Monitoring for Clinical Trials

Safety evaluation is an essential component of clinical trials. To protect study participants, these studies often implement safety stopping rules that will halt the trial if an excessive number of toxicity events occur. Existing safety monitoring methods often treat these events as binary outcomes. A strategy that instead handles these as time-to-event endpoints can offer higher power and a reduced time to signal of excess risk, but must manage additional complexities including censoring and competing risks. We propose the TITE-Safety approach for safety monitoring, which incorporates time-to-event information while handling censored observations and competing risks appropriately. This strategy is applied to develop stopping rules using score tests, Bayesian beta-extended binomial models, and sequential probability ratio tests. The operating characteristics of these methods are studied via simulation for common phase 2 and 3 trial scenarios. Across simulation settings, the proposed techniques offer reductions in expected toxicities of 20% or more compared to binary data methods and maintain the type I error rate near the nominal level across various event time distributions. These methods are demonstrated through a redesign of the safety monitoring scheme for BMT CTN 0601, a single arm, phase 2 trial that evaluated bone marrow transplant as treatment for severe sickle cell disease. Our R package "stoppingrule" offers functions to construct and evaluate these stopping rules, providing valuable tools for trial design to investigators.

stat.ME↗

Nonparametric Bayesian Knockoff Generators for Feature Selection Under Complex Data Structure

The recent proliferation of high-dimensional data, such as electronic health records and genetics data, offers new opportunities to find novel predictors of outcomes. Presented with a large set of candidate features, interest often lies in selecting the ones most likely to be predictive of an outcome for further study. Controlling the false discovery rate (FDR) at a specified level is often desired in evaluating these variables. Knockoff filtering is an innovative strategy for conducting FDR-controlled feature selection. This paper proposes a nonparametric Bayesian model for generating high-quality knockoff copies that can improve the accuracy of predictive feature identification for variables arising from complex distributions, which can be skewed, highly dispersed and/or a mixture of distributions. This paper provides a detailed description for generating knockoff copies from a GDPM model via MCMC posterior sampling. Additionally, we provide a theoretical guarantee on the robustness of the knockoff procedure. Through simulations, the method is shown to identify important features with accurate FDR control and improved power over the popular second-order Gaussian knockoff generator. Furthermore, the model is compared with finite Gaussian mixture knockoff generator in FDR and power. The proposed technique is applied for detecting genes predictive of survival in ovarian cancer patients using data from The Cancer Genome Atlas (TCGA).

stat.ME↗