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Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal
Zhuowei Sun1,2, Hongyuan Cao3
1School of Public Health, Dalian Medical University, Dalian, 116044, Liaoning, China.
Journal of Nonparametric Statistics
|October 6, 2025
Summary
This study introduces a new kernel weighting method for analyzing time-varying coefficients in survival models with intermittent data. The approach provides unbiased estimation, improving accuracy for complex longitudinal covariate analysis.
Area of Science:
- Biostatistics
- Survival Analysis
- Longitudinal Data Analysis
Background:
- Multiplicative hazards models are crucial for survival analysis.
- Intermittently observed longitudinal covariates and time-varying coefficients present estimation challenges.
- Existing methods like 'last value carried forward' can introduce bias.
Purpose of the Study:
- To develop an unbiased estimation method for non-parametric coefficient functions in survival models.
- To address bias issues in models with intermittently observed longitudinal covariates.
- To establish statistical properties and practical utility of the proposed method.
Main Methods:
- Kernel weighting approach for unbiased estimation.
- Asymptotic normality established for fixed time points.
- Simultaneous confidence bands constructed for variation assessment.
Main Results:
- The proposed kernel weighting method yields unbiased estimates.
- Theoretical predictions are supported by simulation studies.
- The method demonstrates favorable performance compared to existing approaches.
Conclusions:
- The kernel weighting approach offers a statistically sound and effective solution for complex survival models.
- The methodology is validated through simulations and illustrated with real-world data.
- This work advances the analysis of time-to-event data with longitudinal, intermittently observed covariates.
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