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The Survival Kit: software to analyze survival data including possibly correlated random effects
G Mészáros1, J Sölkner, V Ducrocq
1Division of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Gregor-Mendel-Str. 33, A-1180 Vienna, Austria. gabor.meszaros@boku.ac.at
Computer Methods and Programs in Biomedicine
|February 13, 2013
Summary
The Survival Kit software offers advanced survival analysis using proportional hazards and frailty models. It handles complex data structures and random effects, with an R interface for user-friendliness.
Area of Science:
- Biostatistics
- Statistical Software Development
Background:
- Survival analysis is crucial for modeling time-to-event data in various scientific fields.
- Proportional hazards and frailty models are advanced statistical techniques for survival data.
- Existing software may lack flexibility in handling complex covariate structures and random effects.
Purpose of the Study:
- To introduce the Survival Kit, a Fortran 90 software for sophisticated survival analysis.
- To provide a flexible tool for proportional hazards and frailty models with single response times.
- To enable the estimation of variance parameters in a Bayesian context, accommodating correlated random effects.
Main Methods:
- The software implements hazard functions as a product of baseline hazard and covariate effects.
- It supports various models including Stratified Cox, grouped data, and Weibull models.
- Random effects can be log-gamma or normally distributed, with options for known or estimated correlation coefficients.
Main Results:
- The Survival Kit enables the analysis of survival data with time-dependent covariates and complex random effects.
- Bayesian estimation of variance parameters is facilitated, including the correlation between random effects.
- The software accommodates pedigree structures through random effects.
Conclusions:
- The Survival Kit is a comprehensive software package for advanced survival analysis.
- Its flexibility in modeling covariates and random effects makes it suitable for complex research.
- The integrated R interface enhances usability and accessibility for researchers.
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