Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process

TL;DR

This paper introduces a new statistical model for predicting events based on monotonic degradation processes, useful in engineering and biomedical fields.

Contribution

A boosting algorithm for first hitting time models using a homogeneous gamma process to capture monotonic degradation.

Findings

The proposed model effectively captures monotonic degradation in real-world engineering and biomedical data.

The algorithm demonstrates strong predictive performance through both simulated and real data examples.

Abstract

In the context of time-to-event analysis, First hitting time methods consider the event occurrence as the ending point of some evolving process. The characteristics of the process are of great relevance for the analysis, which makes this class of models interesting and particularly suitable for applications where something about the degradation path is known. In cases where the degradation can only worsen, a monotonic process is the most suitable choice. This paper proposes a boosting algorithm for first hitting time models based on an underlying homogeneous gamma process to account for the monotonicity of the degradation trend. The predictive power and versatility of the algorithm are shown with real data examples from both engineering and biomedical applications, as well as with simulated examples.

The online version contains supplementary material available at 10.1007/s10985-025-09648-z.

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