Survival and lifetime data analysis with a flexible class of distributions

TL;DR

This paper introduces a versatile new class of continuous distributions for survival analysis, capable of modeling diverse tail behaviors and hazard functions, offering a flexible alternative to traditional models like Weibull and Gamma.

Contribution

It proposes a novel, easily implementable class of distributions derived from two-piece distributions, enhancing modeling flexibility in survival and lifetime data analysis.

Findings

The new distributions effectively capture various tail behaviors.

Simulation studies confirm desirable inferential properties.

Applications demonstrate improved modeling in real-world survival data.

Abstract

We introduce a general class of continuous univariate distributions with positive support obtained by transforming the class of two-piece distributions. We show that this class of distributions is very flexible, easy to implement, and contains members that can capture different tail behaviours and shapes, producing also a variety of hazard functions. The proposed distributions represent a flexible alternative to the classical choices such as the log-normal, Gamma, and Weibull distributions. We investigate empirically the inferential properties of the proposed models through an extensive simulation study. We present some applications using real data in the contexts of time-to-event and accelerated failure time models. In the second kind of applications, we explore the use of these models in the estimation of the distribution of the individual remaining life.