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Published on: October 23, 2020
Fitting semiparametric additive hazards models using standard statistical software
Douglas E Schaubel1, Guanghui Wei
1Department of Biostatistics, University of Michigan, M4039 SPH II, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA. deschau@umich.edu
Biometrical Journal. Biometrische Zeitschrift
|July 20, 2007
Summary
The additive hazards model offers a more suitable choice than the Cox model when covariate effects are additive. This study demonstrates fitting the additive hazards model using standard SAS procedures for liver transplant patient data.
Area of Science:
- Biostatistics
- Survival Analysis
- Medical Informatics
Background:
- The Cox proportional hazards model is standard in biomedical research for estimating covariate effects.
- Its widespread use stems from convenience rather than optimal data fit, potentially overlooking additive covariate effects.
- A lack of direct software support for additive hazards models hinders their adoption.
Purpose of the Study:
- To establish the relationship between the Lin and Ying (1994) additive hazards model and Cox/least squares regression.
- To demonstrate fitting the additive hazards model using existing SAS procedures (phreg, reg).
- To apply the additive hazards model to analyze MELD score and mortality in liver transplant candidates.
Main Methods:
- Established theoretical connections between additive hazards, Cox, and least squares regression models.
- Developed data manipulation techniques to implement the additive hazards model in SAS.
- Applied the fitted additive hazards model to a cohort of liver transplant wait-listed patients.
Main Results:
- Demonstrated that the additive hazards model can be effectively fitted using standard SAS procedures.
- Provided a practical method for researchers to utilize additive hazards modeling.
- The analysis revealed insights into the relationship between MELD score and mortality in the studied patient group.
Conclusions:
- The additive hazards model presents a viable and potentially more appropriate alternative to the Cox model in certain biomedical contexts.
- This work facilitates the application of additive hazards modeling through accessible software solutions.
- The findings support the use of additive hazards for analyzing liver transplant patient survival data.
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