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• W3125328045 abstract "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 paper we discuss a more general approach whereby covariates enter the model through more than one distributional parameter simultaneously (e.g., scale and shape parameters). We refer to this practice as multi-parameter regression (MPR) modelling 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 mpr package in R." @default.
• W3125328045 created "2021-02-01" @default.
• W3125328045 creator A5001187649 @default.
• W3125328045 creator A5023720119 @default.
• W3125328045 date "2016-11-28" @default.
• W3125328045 modified "2023-09-25" @default.
• W3125328045 title "Multi-parameter regression survival modeling: An alternative to proportional hazards" @default.
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• W3125328045 doi "https://doi.org/10.1111/biom.12625" @default.
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• W3125328045 hasPublicationYear "2016" @default.
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