Journal
BIOSTATISTICS
Volume 9, Issue 1, Pages 137-151Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxm018
Keywords
diagnostic tests; mixture models; semi-nonparametric densities; semiparametrics; sensitivity; specificity; transformations
Funding
- NATIONAL CANCER INSTITUTE [R01CA057030, U01CA057030, R37CA057030] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [P30ES009106] Funding Source: NIH RePORTER
- NCI NIH HHS [CO-12400, N01CO12400, R37 CA057030, CA-57030, U01 CA057030, R01 CA057030, R37 CA057030-20] Funding Source: Medline
- NIEHS NIH HHS [P30 ES009106, P30-ES09106] Funding Source: Medline
Ask authors/readers for more resources
For many diseases, it is difficult or impossible to establish a definitive diagnosis because a perfect gold standard may not exist or may be too costly to obtain. In this paper, we propose a method to use continuous test results to estimate prevalence of disease in a given population and to estimate the effects of factors that may influence prevalence. Motivated by a study of human herpesvirus 8 among children with sickle-cell anemia in Uganda, where 2 enzyme immunoassays were used to assess infection status, we fit 2-component multivariate mixture models. We model the component densities using parametric densities that include data transformation as well as flexible transformed models. In addition, we model the mixing proportion, the probability of a latent variable corresponding to the true unknown infection status, via a logistic regression to incorporate covariates. This model includes mixtures of multivariate normal densities as a special case and is able to accommodate unusual shapes and skewness in the data. We assess model performance in simulations and present results from applying various parameterizations of the model to the Ugandan study.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available