Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data
- 1.5k Downloads
Joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data has been proposed to study the relationship between diseases and their related biomarkers. However, statistical inference of the joint latent class modeling approach has proved very challenging due to its computational complexity in seeking maximum likelihood estimates. In this article, we propose a series of composite likelihoods for maximum composite likelihood estimation, as well as an enhanced Monte Carlo expectation–maximization (MCEM) algorithm for maximum likelihood estimation, in the context of joint latent class models. Theoretically, the maximum composite likelihood estimates are consistent and asymptotically normal. Numerically, we have shown that, as compared to the MCEM algorithm that maximizes the full likelihood, not only the composite likelihood approach that is coupled with the quasi-Newton method can substantially reduce the computational complexity and duration, but it can simultaneously retain comparative estimation efficiency.
KeywordsPseudo-likelihood Expectation–maximization algorithm Markov chain Monte Carlo Shared latent class models Two-part models
We sincerely thank two anonymous reviewers, Associate Editor, and Editors for their valuable comments, which had substantially improved this manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views of US Food and Drug Administration.
- Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (2008) National Health and Nutrition Examination Survey Data. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2003–2004, HyattsvilleGoogle Scholar
- Giboney PT (2005) Mildly elevated liver transaminase levels in the asymptomatic patient. Am Fam Physcian 71(6):1105–1110Google Scholar
- Herbstman JB, Sjödin A, Jones R, Kurzon M, Lederman SA, Rauh VA, Needham LL, Wang R, Perera FP (2008) Prenatal exposure to PBDEs and neurodevelopment. Epidemiology 19(6):S348Google Scholar
- Kratz A, Ferraro M, Sluss PM, Lewandrowski KB (2004) Case records of the Massachusetts general hospital: laboratory values. N Engl J Med 351(15):1549–1563Google Scholar
- Main KM, Kiviranta H, Virtanen HE, Sundqvist E, Tuomisto JT, Tuomisto J, Vartiainen T, Skakkebaek NE, Toppari J (2007) Flame retardants in placenta and breast milk and cryptorchidism in newborn boys. Environ Health Perspect 115(10):1519–1526Google Scholar