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Estimation of conditional power for cluster-randomized trials with interval-censored endpoints
Kaitlyn Cook1, Rui Wang1,2
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Biometrics
|August 26, 2020
Summary
This study introduces a new method for conditional power estimation in cluster-randomized trials (CRTs) with correlated, interval-censored data. The approach improves interim monitoring and futility assessments for infectious disease prevention studies.
Area of Science:
- Biostatistics
- Epidemiology
- Clinical Trials
Background:
- Cluster-randomized trials (CRTs) in infectious disease prevention often generate correlated and interval-censored data.
- Dependencies within clusters and intermittent data collection complicate interim monitoring and futility assessments.
Purpose of the Study:
- To propose a flexible framework for conditional power estimation in CRTs with correlated, interval-censored outcomes.
- To address the challenges in interim analysis for infectious disease prevention trials.
Main Methods:
- Developed a semiparametric approach using a shared frailty model to estimate cluster-specific survival distributions.
- Characterized the relationship between marginal and cluster-conditional survival functions.
- Projected survival curves to the end of the study incorporating event process changes and cluster dependency.
Main Results:
- The proposed method successfully generates correlated interval-censored data for conditional power calculation.
- Conditional power is estimated as the rejection rate of the null hypothesis across simulated full datasets.
- Simulations demonstrated the method's performance, and it was applied to an HIV prevention CRT.
Conclusions:
- The developed framework provides a robust method for conditional power estimation in CRTs with complex data structures.
- This enhances the reliability of interim monitoring and futility assessments in public health intervention studies.
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