swMATH ID: 21870
Software Authors: Burke, K.; MacKenzie, G.
Description: Multi-parameter regression survival modeling: an alternative to proportional hazards. It is standard practice for covariates to enter a parametric model through a single distributional parameter of interest, for example, the scale parameter in many standard survival models. Indeed, the well-known proportional hazards model is of this kind. In this article, we discuss a more general approach whereby covariates enter the model through {it more than one} distributional parameter simultaneously (e.g., scale {it and} shape parameters). We refer to this practice as “multi-parameter regression” (MPR) modeling and explore its use in a survival analysis context. We find that multi-parameter regression leads to more flexible models which can offer greater insight into the underlying data generating process. To illustrate the concept, we consider the two-parameter Weibull model which leads to time-dependent hazard ratios, thus relaxing the typical proportional hazards assumption and motivating a new test of proportionality. A novel variable selection strategy is introduced for such multi-parameter regression models. It accounts for the correlation arising between the estimated regression coefficients in two or more linear predictors – a feature which has not been considered by other authors in similar settings. The methods discussed have been implemented in the { t mpr} package in { t R}.
Homepage: https://cran.r-project.org/web/packages/mpr/index.html
Source Code:  https://github.com/cran/mpr
Dependencies: R
Keywords: crossing hazards; converging hazards; diverging hazards; multi-parameter regression; non-proportional hazards; survival analysis; time-dependent effects; variable selection
Related Software: R; GAMLSS; distreg.vis; RobStatTM; alr3; glmnet; robustbase; selectiveInference; timereg; hnp; gamair; GJRM; trust
Cited in: 6 Documents

Cited in 1 Field

6 Statistics (62-XX)

Citations by Year