mpr 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; timereg; hnp; GJRM; trust; gamair Cited in: 5 Publications Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year Multi-parameter regression survival modeling: an alternative to proportional hazards. Zbl 1372.62056Burke, K.; MacKenzie, G. 2017 all top 5 Cited by 14 Authors 2 MacKenzie, Gilbert 1 Aeberhard, William H. 1 Al-tawarah, Yasin 1 Blagojevic-Bucknall, Miliça 1 Cantoni, Eva 1 Demétrio, Clarice Garcia Borges 1 Godoy, Wesley A. C. 1 Hinde, John P. 1 Marra, Giampiero 1 Moral, Rafael A. 1 Munezero, Parfait 1 Ortega, Edwin Moises Marcos 1 Peng, Defen 1 Radice, Rosalba Cited in 5 Serials 1 Biometrics 1 Computational Statistics 1 Journal of Applied Statistics 1 Statistics and Computing 1 Japanese Journal of Statistics and Data Science Cited in 1 Field 5 Statistics (62-XX) Citations by Year