Work

Pseudo-observation regression for sequentially truncated data

Year: 2026

Type: article

Abstract: In observational cohort studies with complex sampling schemes, truncation arises when the time to the event of interest is observed only when it falls below or exceeds another random time, i.e., the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this extension of the traditional paradigm as sequential truncation. Nonparametric and semiparametric maximum likelihood estimators have been developed recently to estimate the distribution of the event time of interest in the presence of sequential truncation. In this paper, we develop methods for regression modeling in this complex setting using the tool of pseudo-observations. Pseudo-observations are jackknife-like constructs that estimate an individual's contribution to an estimand. They are convenient as they are based on an estimator for the unconditional distribution of the event time. However, the simple pseudo-observation method may not be valid when the truncation depends on the covariates that also explain the time to event of interest, among other constraints. To address this limitation, we also consider a modified pseudo-observation approach. We develop both simple and modified pseudo-observation methods for Cox and accelerated failure time (AFT) models. We evaluate the proposed methods in simulation studies and apply them to an Alzheimer's disease cohort study.

Source: Biometrics

DOI: 10.1093/biomtc/ujag084

Authors: Jing Qian, Erik T Parner, Morten Overgaard, Rebecca A Betensky

Institutions: University of Massachusetts Amherst, Aarhus University, New York University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute on Aging, National Institute of Neurological Disorders and Stroke