Sequential monitoring of time-to-event safety endpoints in clinical trials
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces time-to-event analysis for clinical trial safety monitoring, outperforming traditional binary methods by reducing expected toxicities up to 20%. The developed R package "stoppingrule" aids in designing effective safety stopping rules.
Area Of Science
- Clinical Trials
- Biostatistics
- Pharmacovigilance
Background
- Safety monitoring is critical in Phase II and III clinical trials to protect patients from treatment toxicity.
- Current stopping rules often use binary toxicity data, potentially missing opportunities for more efficient risk identification.
- Time-to-event data, when available, may offer increased statistical power and reduced trial duration for detecting excess risks.
Purpose Of The Study
- To investigate and compare statistical methods for safety monitoring using time-to-event data in clinical trials.
- To develop and illustrate an R software package, "stoppingrule", for designing and evaluating these novel stopping rules.
- To assess the performance of time-to-event based stopping rules against traditional binary approaches.
Main Methods
- Evaluated performance metrics of safety stopping rules based on Wang-Tsiatis tests, Bayesian Gamma-Poisson models, and sequential probability ratio tests.
- Developed and utilized the R package "stoppingrule" for designing and assessing time-to-event based stopping rules.
- Applied these methods to a Phase II clinical trial scenario (NCT01998633) for bone marrow transplant.
Main Results
- Time-to-event methods demonstrated meaningful reductions in expected toxicities, up to 20%, compared to binary toxicity approaches.
- Aggressive early monitoring methods (e.g., Gamma-Poisson with weak priors) generally showed lower toxicity counts and power.
- Certain tests (Pocock, maximized sequential probability ratio test) exhibited high early stopping but reduced overall power and increased toxicities.
Conclusions
- Time-to-event based safety monitoring offers superior performance in minimizing patient toxicity exposure.
- Recommends the Gamma-Poisson model with weak priors or truncated sequential probability ratio test for constructing robust safety stopping rules.
- The "stoppingrule" R package provides a valuable tool for researchers to design and assess advanced safety monitoring procedures in clinical trials.
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