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Published in: Lifetime Data Analysis 4/2023

20-05-2023

Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study

Authors: Daewoo Pak, Jing Ning, Richard J. Kryscio, Yu Shen

Published in: Lifetime Data Analysis | Issue 4/2023

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Abstract

The Nun study is a well-known longitudinal epidemiology study of aging and dementia that recruited elderly nuns who were not yet diagnosed with dementia (i.e., incident cohort) and who had dementia prior to entry (i.e., prevalent cohort). In such a natural history of disease study, multistate modeling of the combined data from both incident and prevalent cohorts is desirable to improve the efficiency of inference. While important, the multistate modeling approaches for the combined data have been scarcely used in practice because prevalent samples do not provide the exact date of disease onset and do not represent the target population due to left-truncation. In this paper, we demonstrate how to adequately combine both incident and prevalent cohorts to examine risk factors for every possible transition in studying the natural history of dementia. We adapt a four-state nonhomogeneous Markov model to characterize all transitions between different clinical stages, including plausible reversible transitions. The estimating procedure using the combined data leads to efficiency gains for every transition compared to those from the incident cohort data only.

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Appendix
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Metadata
Title
Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study
Authors
Daewoo Pak
Jing Ning
Richard J. Kryscio
Yu Shen
Publication date
20-05-2023
Publisher
Springer US
Published in
Lifetime Data Analysis / Issue 4/2023
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-023-09602-x

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