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Attributable fraction functions for censored event times.
Li Chen1, D Y Lin, Donglin Zeng
1Department of Biostatistics, CB# 7420 , University of North Carolina , Chapel Hill, North Carolina 27599-7420 , U.S.A. lchen@bios.unc.edu lin@bios.unc.edu dzeng@bios.unc.edu.
Biometrika
|August 20, 2013
Summary
This study introduces new methods for estimating attributable fractions over time in cohort studies with censored data. These advanced techniques accurately assess the population impact of risk factors on disease incidence.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Attributable fractions quantify risk factor impact on disease incidence.
- Static measures are insufficient when time-to-event is critical.
- Need for dynamic attributable fraction estimation in cohort studies.
Purpose of the Study:
- To develop nonparametric and semiparametric estimators for attributable fraction functions.
- To extend attributable fraction estimation to time-dependent scenarios.
- To analyze cohort studies with censored event time data.
Main Methods:
- Nonparametric estimation of attributable fraction functions.
- Semiparametric estimation using proportional hazards and transformation models.
- Handling of censored event time data in cohort studies.
Main Results:
- Proposed estimators are consistent, asymptotically normal, and efficient.
- Methods demonstrate strong performance in simulations.
- Application to a cardiovascular health study.
Conclusions:
- The developed methods provide reliable estimation of time-dependent attributable fractions.
- These tools enhance understanding of risk factor impact over time.
- The study bridges statistical estimation with causal inference concepts.
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