ArticleBiometrika2024
Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data.
Article in Biometrika, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Proportional Hazards Regression for Interval-Censored Outcomes With an Interval-Censored Covariate.Statistics in medicine · 2026Article
- Discrimination performance in illness-death models with interval-censored disease data.Statistical methods in medical research · 2026Article
- Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.Biostatistics (Oxford, England) · 2026Article
- Nonparametric Estimation of Transition Intensities in Interval-Censored Markov Multistate Models Without Loops.Statistics in medicine · 2025Article
- Post-selection inference for the Cox model with interval-censored data.Scandinavian journal of statistics, theory and applications · 2025Article
- Joint modeling of multistate survival processes with informative examination scheme: application to progressions in diabetes.BMC medical research methodology · 2025Article
- Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State.Journal of the American Statistical Association · 2025Article
- JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA.The annals of applied statistics · 2024Article
- A discrete approximation method for modeling interval-censored multistate data.Statistics in medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Interval-censored multistate data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring and develop a stable expectation-maximization algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.