title: Joint analysis of prevalence and incidence data using conditional likelihood.
abstract: Disease prevalence is the combined result of duration, disease incidence, case fatality, and other mortality. If information is available on all these factors, and on fixed covariates such as genotypes, prevalence information can be utilized in the estimation of the effects of the covariates on disease incidence. Study cohorts that are recruited as cross-sectional samples and subsequently followed up for disease events of interest produce both prevalence and incidence information. In this paper, we make use of both types of information using a likelihood, which is conditioned on survival until the cross section. In a simulation study making use of real cohort data, we compare the proposed conditional likelihood method to a standard analysis where prevalent cases are omitted and the likelihood expression is conditioned on healthy status at the cross section.
authors.affiliation: Department of Chronic Disease Prevention, National Institute for Health and Welfare, Mannerheimintie 166, 00300 Helsinki, Finland. olli.saarela@thl.fi
authors.forename: Olli
authors.initials: O
authors.lastname: Saarela
authors.forename: Sangita
authors.initials: S
authors.lastname: Kulathinal
authors.forename: Juha
authors.initials: J
authors.lastname: Karvanen
mesh_terms: D000328:Adult; D001699:Biometry; D015331:Cohort Studies; D003327:Coronary Disease / Q000453:epidemiology; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005387:Finland / Q000453:epidemiology; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D055815:Young Adult