ArticleBiostatistics (Oxford, England)2021
Independence conditions and the analysis of life history studies with intermittent observation.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- Selection processes, transportability, and failure time analysis in life history studies.Biostatistics (Oxford, England) · 2025Article
- Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams.Biostatistics (Oxford, England) · 2025Article
- Variable Selection for Progressive Multistate Processes Under Intermittent Observation.Statistics in medicine · 2025Article
- Methodological challenges in studying disease processes using observational cohort data.Japanese journal of statistics and data science · 2025Article
- Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data.Biometrika · 2024Article
- The polytomous discrimination index for prediction involving multistate processes under intermittent observation.Statistics in medicine · 2022Article
- Assessing the accuracy of predictive models with interval-censored data.Biostatistics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Multistate models provide a powerful framework for the analysis of life history processes when the goal is to characterize transition intensities, transition probabilities, state occupancy probabilities, and covariate effects thereon. Data on such processes are often only available at random visit times occurring over a finite period. We formulate a joint multistate model for the life history process, the recurrent visit process, and a random loss to follow-up time at which the visit process terminates. This joint model is helpful when discussing the independence conditions necessary to justify the use of standard likelihoods involving the life history model alone and provides a basis for analyses that accommodate dependence. We consider settings with disease-driven visits and routinely scheduled visits and develop likelihoods that accommodate partial information on the types of visits. Simulation studies suggest that suitably constructed joint models can yield consistent estimates of parameters of interest even under dependent visit processes, providing the models are correctly specified; identifiability and estimability issues are also discussed. An application is given to a cohort of individuals attending a rheumatology clinic where interest lies in progression of joint damage.
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