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Cross-ratio estimation for bivariate failure times with left truncation
Tianle Hu1, Xihong Lin, Bin Nan
1Eli Lilly and Company, Indianapolis, IN, 6285, USA, hu_tianle@lilly.com.
Lifetime Data Analysis
|May 24, 2013
Summary
This study addresses biased cross-ratio estimates in follow-up studies by accounting for left truncation. A modified estimation method ensures accurate dependence measures for bivariate failure times.
Area of Science:
- Statistics
- Survival Analysis
- Biostatistics
Background:
- The cross-ratio is a key measure for bivariate failure time dependence.
- Left truncation in follow-up data can bias cross-ratio estimation.
- Accurate dependence measures are crucial for survival data analysis.
Purpose of the Study:
- To develop an accurate method for estimating the cross-ratio in the presence of left truncation.
- To extend existing methodologies to handle delayed entry in bivariate survival data.
- To ensure reliable statistical inference for dependent failure times.
Main Methods:
- Extending the Hu et al. (2011) method for cross-ratio estimation.
- Modifying risk sets and indicators to accommodate left-truncated bivariate failure times.
- Utilizing established asymptotic techniques for theoretical validation.
Main Results:
- The proposed method provides unbiased cross-ratio estimates.
- The modified approach yields estimates with desirable asymptotic properties.
- Numerical studies confirm the efficacy of the developed estimation procedure.
Conclusions:
- Accounting for left truncation is essential for accurate cross-ratio estimation.
- The extended method offers a robust approach for analyzing dependent failure times with delayed entry.
- This work improves statistical tools for survival data analysis in complex scenarios.
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