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Insights on the robust variance estimator under recurrent-events model
Hussein R Al-Khalidi1, Yili Hong, Thomas R Fleming
1Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina 27705, USA.
Biometrics
|March 23, 2011
Summary
Recurrent event models in medical research may not always show reduced variance with longer trials. This study investigates this phenomenon in arrhythmia and diabetes data, offering insights into sample size calculations.
Area of Science:
- Biostatistics
- Clinical Trials
- Medical Research
Background:
- Recurrent events are frequently observed in longitudinal medical studies.
- Cardiovascular patients with implantable cardioverter defibrillators (ICDs) often experience recurrent arrhythmic events.
Purpose of the Study:
- To investigate why robust variance in recurrent-event models does not always diminish with extended trial durations.
- To analyze this phenomenon using real-world datasets and simulations.
Main Methods:
- Analysis of large datasets from an arrhythmia clinical trial and a diabetes study.
- Application of recurrent-event modeling.
- Conducting simulations to explore the phenomenon.
Main Results:
- Demonstrated that the robust variance in recurrent-event models does not consistently decrease as trial duration increases.
- Identified specific conditions and factors influencing this variance behavior.
Conclusions:
- The assumption of diminishing robust variance with extended follow-up in recurrent-event analysis is not universally applicable.
- Findings provide crucial insights for refining sample size calculations and study designs in recurrent-event data analysis.
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