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Biostatistics Advance Access originally published online on May 20, 2009
Biostatistics 2009 10(3):575-587; doi:10.1093/biostatistics/kxp013
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Joint analysis of prevalence and incidence data using conditional likelihood

Olli Saarela*

Department of Chronic Disease Prevention, National Institute for Health and Welfare, Mannerheimintie 166, 00300 Helsinki, Finland
olli.saarela{at}thl.fi

Sangita Kulathinal

Indic Society for Education and Development (INSEED), Nashik 422 011, India and Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland

Juha Karvanen

Department of Chronic Disease Prevention, National Institute for Health and Welfare, Mannerheimintie 166, 00300 Helsinki, Finland

* To whom correspondence should be addressed.

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.

Keywords: Ascertainment correction; Conditional likelihood; Incidence; Left censoring; Left truncation; Prevalence

Received November 18, 2008; revised February 20, 2009; revised March 24, 2009; accepted for publication April 24, 2009.


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