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Penalized variable selection in multi-parameter regression survival modeling
Date
2023
Abstract
Standard survival models such as the proportional hazards model contain a single regression component, corresponding to the scale of the hazard. In contrast, we consider the so-called “multi-parameter regression” approach whereby covariates enter the model through multiple distributional parameters simultaneously, for example, scale and shape parameters. This approach has previously been shown to achieve flexibility with relatively low model complexity. However, beyond a stepwise type selection method, variable selection methods are underdeveloped in the multi-parameter regression survival modeling setting. Therefore, we propose penalized multi-parameter regression estimation procedures using the following penalties: least absolute shrinkage and selection operator, smoothly clipped absolute deviation, and adaptive least absolute shrinkage and selection operator. We compare these procedures using extensive simulation studies and an application to data from an observational lung cancer study; the Weibull multi-parameter regression model is used throughout as a running example.
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Publisher
Sage Publications
Citation
Statistical Methods in Medical Research, 2023
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Funding Information
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author was funded by the Irish Research Council. The second author was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1F1A1A01056987). The third author was supported by the Confirm Smart Manufacturing Centre (https://confirm.ie/) funded by Science Foundation Ireland (Grant Number: 16/RC/3918)
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Article
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https://creativecommons.org/licenses/by-nc-sa/4.0/