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, Yu Shen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)752-768
Number of pages17
JournalLifetime Data Analysis
Issue number4
StatePublished - Oct 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Combined cohort data
  • Incident cohort
  • Interval censoring
  • Left truncation
  • Multistate model
  • Prevalent cohort

ASJC Scopus subject areas

  • Applied Mathematics


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