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

Daewoo Pak, Jing Ning, Richard J. Kryscio and Yu Shen ()
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Daewoo Pak: Yonsei University
Jing Ning: The University of Texas MD Anderson Cancer Center
Richard J. Kryscio: University of Kentucky
Yu Shen: The University of Texas MD Anderson Cancer Center

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 4, No 4, 752-768

Abstract: 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.

Keywords: Combined cohort data; Incident cohort; Interval censoring; Left truncation; Multistate model; Prevalent cohort (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10985-023-09602-x

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