Work

Regression Models with Interval-Censored Variables

Year: 2025

Type: review

Abstract: Survival analysis is essential for modeling time-to-event data across various fields, including medicine, engineering, and the social sciences. A major challenge in this field is handling censored data, particularly partly interval-censored data, where event times are either precisely recorded or only known to fall within a specific interval. Proper statistical modeling of such data is crucial for drawing valid conclusions and making accurate predictions. This article reviews regression models for analyzing interval-censored responses and their implementation in R. Following an introduction to the nonparametric maximum likelihood estimator, we focus on four major regression models: the accelerated failure time model, the proportional hazards model, the proportional odds model, and the generalized odds-rate model. For each, we review the state of the art, outline its methodology, discuss implementation strategies, and illustrate practical applications using real-world data. The article concludes with a discussion of current challenges, alternative modeling approaches, and potential directions for future research.

Source: Annual Review of Statistics and Its Application

DOI: 10.1146/annurev-statistics-042424-103337

Authors: Guadalupe Gómez Melis, Ramon Oller, Klaus Langohr

Institutions: Universitat Politècnica de Catalunya, Universitat de Vic - Universitat Central de Catalunya

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Funders: Ministerio de Ciencia, Innovación y Universidades, Generalitat de Catalunya, Agència de Gestió d'Ajuts Universitaris i de Recerca

Award: G6838057026