Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study.
Lifetime Data Anal
; 29(4): 752-768, 2023 10.
Article
en En
| MEDLINE
| ID: mdl-37210470
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Demencia
/
Monjas
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Humans
Idioma:
En
Revista:
Lifetime Data Anal
Año:
2023
Tipo del documento:
Article