TITE-safety: a robust time-to-event safety monitoring approach for clinical trials. Biometrics 2026 Apr 09;82(2)
Date
05/27/2026Pubmed ID
42201841Pubmed Central ID
PMC13215099DOI
10.1093/biomtc/ujag097Scopus ID
2-s2.0-105040601853 (requires institutional sign-in at Scopus site)Abstract
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 usually treat these events as binary outcomes. A strategy that instead handles these as time-to-event (TITE) 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 TITE information while handling repeated analyses, 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. 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 for various event time distributions. These methods are demonstrated through a redesign of the safety monitoring scheme for Blood and Marrow Transplant Clinical Trials Network 0601, a single arm, phase 2 trial that evaluated bone marrow transplant as treatment for sickle cell disease. Our R package "stoppingrule" offers functions to construct and evaluate these stopping rules, providing valuable tools for trial design to investigators.
Author List
Martens MJ, Lian Q, Logan BRAuthors
Brent R. Logan PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinMichael Martens PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Anemia, Sickle CellBayes Theorem
Bone Marrow Transplantation
Clinical Trials as Topic
Computer Simulation
Endpoint Determination
Humans
Models, Statistical
