Parametric modelling of prevalent cohort data with uncertainty in the measurement of the initial onset date.
Lifetime Data Anal
; 26(2): 389-401, 2020 04.
Article
em En
| MEDLINE
| ID: mdl-31376057
ABSTRACT
In prevalent cohort studies with follow-up, if disease duration is the focus, the date of onset must be obtained retrospectively. For some diseases, such as Alzheimer's disease, the very notion of a date of onset is unclear, and it can be assumed that the reported date of onset acts only as a proxy for the unknown true date of onset. When adjusting for onset dates reported with error, the features of left-truncation and potential right-censoring of the failure times must be modeled appropriately. Under the assumptions of a classical measurement error model for the onset times and an underlying parametric failure time model, we propose a maximum likelihood estimator for the failure time distribution parameters which requires only the observed backward recurrence times. Costly and time-consuming follow-up may therefore be avoided. We validate the maximum likelihood estimator on simulated datasets under varying parameter combinations and apply the proposed method to the Canadian Study of Health and Aging dataset.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Estudos de Coortes
/
Modelos Estatísticos
/
Incerteza
Tipo de estudo:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Lifetime Data Anal
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Canadá