Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data

Abdisalam Hassan Muse (), Samuel Mwalili, Oscar Ngesa, Christophe Chesneau (), Afrah Al-Bossly and Mahmoud El-Morshedy
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Abdisalam Hassan Muse: Institute for Basic Sciences, Technology and Innovation (PAUSTI), Pan African University, Nairobi 62000-00200, Kenya
Samuel Mwalili: Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi 62000-00200, Kenya
Oscar Ngesa: Department of Mathematics and Physical Sciences, Taita Taveta University, Voi 635-80300, Kenya
Christophe Chesneau: Department of Mathematics, LMNO, CNRS-Université de Caen, Campus II, Science 3, 14032 Caen, France
Afrah Al-Bossly: Department of Mathematics, College of Science and Humanities in AL-Kharj, Prince Sattam Bin Abdulaziz University, AL-Kharj 11942, Saudi Arabia
Mahmoud El-Morshedy: Department of Mathematics, College of Science and Humanities in AL-Kharj, Prince Sattam Bin Abdulaziz University, AL-Kharj 11942, Saudi Arabia

Mathematics, 2022, vol. 10, issue 20, 1-41

Abstract: In this study, we consider a general, flexible, parametric hazard-based regression model for censored lifetime data with covariates and term it the “general hazard (GH)” regression model. Some well-known models, such as the accelerated failure time (AFT), and the proportional hazard (PH) models, as well as the accelerated hazard (AH) model accounting for crossed survival curves, are sub-classes of this general hazard model. In the proposed class of hazard-based regression models, a covariate’s effect is identified as having two distinct components, namely a relative hazard ratio and a time-scale change on hazard progression. The new approach is more adaptive to modelling lifetime data and could give more accurate survival forecasts. The nested structure that includes the AFT, AH, and PH models in the general hazard model may offer a numerical tool for identifying which of them is most appropriate for a certain dataset. In this study, we propose a method for applying these various parametric hazard-based regression models that is based on a tractable parametric distribution for the baseline hazard, known as the generalized log-logistic (GLL) distribution. This distribution is closed under all the PH, AH, and AFT frameworks and can incorporate all of the basic hazard rate shapes of interest in practice, such as decreasing, constant, increasing, V-shaped, unimodal, and J-shaped hazard rates. The Bayesian and frequentist approaches were used to estimate the model parameters. Comprehensive simulation studies were used to evaluate the performance of the proposed model’s estimators and its nested structure. A right-censored cancer dataset is used to illustrate the application of the proposed approach. The proposed model performs well on both real and simulation datasets, demonstrating the importance of developing a flexible parametric general class of hazard-based regression models with both time-independent and time-dependent covariates for evaluating the hazard function and hazard ratio over time.

Keywords: survival analysis; proportional hazard model; oncology data; accelerated hazard model; generalized log-logistic distribution; Bayesian approach; accelerated failure time model; general hazard model; maximum likelihood estimation; censored data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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