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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A Bayesian proportional hazards regression model with non-ignorably missing time-varying covariates
Patrick T Bradshaw1, Joseph G Ibrahim, Marilie D Gammon
1Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. patrickb@email.unc.edu
Statistics in Medicine
|October 21, 2010
Summary
Missing data in time-to-event studies can bias results. This study introduces a Bayesian selection model for time-varying covariates, finding weight gain linked to poorer breast cancer survival.
Area of Science:
- Biostatistics
- Epidemiology
- Survival Analysis
Background:
- Missing covariate data is prevalent in longitudinal observational studies.
- Incomplete data, particularly when non-ignorable, can introduce bias and reduce efficiency in survival analyses.
- Existing methods often do not adequately address non-ignorably missing time-varying covariates.
Purpose of the Study:
- To develop a statistical model for proportional hazards regression that accommodates non-ignorably missing time-varying covariates.
- To provide a robust method for analyzing time-to-event data with complex missingness patterns.
Main Methods:
- A fully Bayesian selection model was developed for proportional hazards regression.
- The Gibbs sampler implemented in WinBUGS was used for posterior estimation of model parameters.
- The model was applied to analyze the association between weight change and survival in breast cancer patients.
Main Results:
- Post-diagnosis weight gain was significantly associated with reduced all-cause and breast cancer-specific survival.
- Sensitivity analyses indicated that assumptions about the missing data mechanism can influence results.
- Complete-case analysis produced substantially different findings compared to the proposed model.
Conclusions:
- The developed Bayesian selection model effectively handles non-ignorably missing time-varying covariates in survival analysis.
- Weight gain after breast cancer diagnosis is a potential prognostic factor.
- Careful consideration of missing data mechanisms is crucial for accurate survival analyses.
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