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Published on: October 23, 2020
Marginal analysis of correlated failure time data with informative cluster sizes
Xiuyu J Cong1, Guosheng Yin, Yu Shen
1Department of Biometrics and Data Management, Boehringer Ingelheim Pharmaceuticals, P.O. Box 368, Ridgefield, Connecticut 06877, USA. xcong@rdg.boehringer-ingelheim.com
Biometrics
|September 11, 2007
Summary
This study introduces novel methods for analyzing correlated survival data, accounting for informative cluster sizes using within-cluster resampling (WCR) and weighted score function (WSF) approaches. These methods provide robust statistical properties for reliable outcome prediction.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Correlated survival data presents unique analytical challenges.
- Cluster sizes can be informative, potentially biasing traditional survival models.
- Accurate modeling is crucial for understanding time-to-event outcomes in clustered data.
Purpose of the Study:
- To develop and evaluate statistical methods for modeling correlated survival data with informative cluster sizes.
- To assess the performance of within-cluster resampling (WCR) and weighted score function (WSF) methods.
- To provide reliable estimators for regression coefficients and baseline hazard functions.
Main Methods:
- Utilized a within-cluster resampling (WCR) approach for correlated survival data.
- Employed a weighted score function (WSF) method, incorporating inverse cluster sizes as weights.
- Derived large sample properties including consistency and asymptotic normality under the Cox proportional hazards model.
Main Results:
- Established theoretical properties for WCR estimators, including consistency and asymptotic normality.
- Demonstrated weak convergence for the estimated baseline cumulative hazard function.
- Simulation studies confirmed the finite-sample performance of the proposed estimators.
Conclusions:
- The WCR and WSF methods offer robust approaches for modeling correlated survival data with informative cluster sizes.
- The derived theoretical properties support the validity and reliability of these statistical methods.
- Application to a dental study illustrates the practical utility of the proposed techniques.
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