Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events

Abstract

Clinical prediction models are commonly utilized in clinical practice to screen high-risk patients. This enables healthcare professionals to initiate interventions aimed at delaying or preventing adverse medical events. Nevertheless, the majority of these models focus on calculating probabilities or risk scores for medical events. This information can pose challenges for patients to comprehend, potentially causing delays in their treatment decision-making process. Our paper presents a statistical methodology and protocol for the utilization of a Weibull accelerated failure time (AFT) model in predicting the time until a health-related event occurs. While this prediction technique is widely employed in engineering reliability studies, it is rarely applied to medical predictions, particularly in the context of predicting survival time. Furthermore, we offer a practical demonstration of the implementation of this prediction method using a publicly available dataset.