AI Article Synopsis
- Disease prevalence depends on several factors like how long people have the disease, how often new cases occur, and how many people die from it.
- Researchers can use prevalence data alongside other fixed factors (like genetic information) to better understand how these factors influence new disease cases.
- The study involves using a statistical method that considers both prevalence and incidence together, comparing it to traditional methods which ignore prevalent cases and only focus on healthy individuals at the start of the study.
Article 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.
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| http://dx.doi.org/10.1093/biostatistics/kxp013 | DOI Listing |
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