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Proportional hazards model with a change point for clustered event data
Yu Deng1, Donglin Zeng1, Jinying Zhao2
1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A.
Biometrics
|March 4, 2017
Summary
This study introduces a new statistical model to identify critical thresholds for disease risk factors in family health studies. This helps in better disease prediction and prevention strategies.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Family data with survival endpoints are common in epidemiology.
- Disease risk can change abruptly at specific risk factor thresholds.
- Identifying these thresholds is crucial for risk prediction and prevention.
Purpose of the Study:
- To propose a novel change-point proportional hazards model for clustered event data.
- To estimate unknown thresholds of continuous variables within regression models.
- To develop statistical tests for detecting the existence of such change points.
Main Methods:
- Utilized marginal pseudo-partial likelihood for estimation of regression coefficients and change points.
- Developed a supremum test using robust score statistics to test for change points.
- Employed m out of n bootstrap for inference on the change point parameter.
Main Results:
- Established consistency and asymptotic distributions for the proposed estimators.
- Demonstrated the finite-sample performance through extensive simulation studies.
- Successfully applied the methods to the Strong Heart Family Study dataset.
Conclusions:
- The proposed change-point model effectively identifies critical risk factor thresholds in clustered survival data.
- The statistical methods provide reliable inference for change point estimation and hypothesis testing.
- This approach enhances disease risk prediction and prevention in epidemiological research.
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