Work

τ$\tau$‐Inflated Beta Regression Model for Estimating τ$\tau$‐Restricted Means and Event‐Free Probabilities for Censored Time‐to‐Event Data

Year: 2024

Type: article

Abstract: ABSTRACT In this research, we propose analysis of ‐restricted censored time‐to‐event data via a ‐inflated beta regression (‐IBR) model. The outcome of interest is , where and are the time‐to‐event and follow‐up duration, respectively. Our analysis goals include estimation and inference related to ‐restricted mean survival time (‐RMST) values and event‐free probabilities at that address the censored nature of the data. In this setting, it is common to observe many individuals with , a point mass that is typically overlooked in ‐restricted event‐time analyses. Our proposed ‐IBR model is based on a decomposition of into . We model the mean of this latter expression using joint logistic and beta regression models that are fit using an expectation‐maximization algorithm. An alternative multiple imputation (MI) algorithm for fitting the ‐IBR model has the additional advantage of producing uncensored datasets for analysis. Simulations indicate excellent performance of the ‐IBR model(s), and corresponding ‐RMST estimates, in independent and dependent censoring settings. We apply our method to the Azithromycin for Prevention of Chronic Obstructive Pulmonary Disease (COPD) Exacerbations Trial. In addition to ‐IBR model results providing a nuanced understanding of the treatment effect, visually appealing heatmaps of the ‐restricted event times based on our MI datasets are given, a visualization not typically available for censored time‐to‐event data.

Source: Biometrical Journal

DOI: 10.1002/bimj.70009

Authors: Yizhuo Wang, Susan Murray

Institution: University of Michigan

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,560