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Continuous event monitoring via a Bayesian predictive approach
Jianing Di1, Daniel Wang1, H Robert Brashear1
1Janssen Research and Development, LLC, USA.
Pharmaceutical Statistics
|December 9, 2015
Summary
This study introduces a Bayesian predictive approach for continuous monitoring of event rates in clinical trials. It accurately estimates cumulative incidence, even with limited early data, improving safety and efficacy assessments.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Pharmacovigilance
Background:
- Continuous monitoring of event incidence rates is crucial for clinical trial decision-making, impacting participant safety and trial outcomes.
- Estimating cumulative event incidence before trial completion is challenging due to evolving event profiles and censored data, potentially causing bias.
- Existing methods may struggle with limited early-stage data, necessitating robust approaches for timely signal detection.
Purpose of the Study:
- To develop and evaluate a Bayesian predictive approach for continuous monitoring of event incidence rates in clinical trials.
- To address the bias introduced by censored subjects in early trial stages when estimating cumulative event incidence.
- To provide a method for early signal detection and safety monitoring using prior knowledge and observed data.
Main Methods:
- A Bayesian predictive approach is proposed, integrating expert prior knowledge on event frequency and timing with observed clinical trial data.
- Event-free subjects are probabilistically accounted for during interim analyses using prior-derived probabilities.
- The method's performance is assessed through simulations and a case study involving adverse event monitoring in an Alzheimer's disease trial.
Main Results:
- The proposed Bayesian approach effectively estimates cumulative event incidence rates, mitigating bias from censored data in early trial phases.
- The predictive method demonstrates utility in early signal detection, especially with limited available information.
- Simulations and case study application confirm the approach's viability for continuous safety monitoring in clinical trials.
Conclusions:
- The Bayesian predictive approach offers a valuable tool for robust event incidence monitoring in clinical trials, enhancing decision-making.
- This method improves the accuracy of cumulative incidence estimation, particularly in the early stages of a study.
- The approach supports both early-stage signal detection and ongoing safety surveillance by data monitoring committees.
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