STATISTICAL METHODS FOR INDIVIDUALIZED HEALTH: ETIOLOGY, DIAGNOSIS, AND INTERVENTION EVALUATION
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Date
2014-09-15
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Johns Hopkins University
Abstract
The term individualized health represents a goal of the next generation of health system: to treat the right person, at the right place, at the right time taking account of the individuals' characteristics, circumstances and preferences. To advance this goal, a new partnership of statistical and biomedical science is needed to intelligently use information to better understand disease etiology, to improve diagnoses and treatment decisions and to accurately evaluate health interventions. In two parts, this thesis addresses statistical methods in support of the individualized health goal.
In Part I, the key objective is to characterize an individual's underlying health state given imprecise measurements. We assume that the health states can be usefully represented by categorical latent variables. We describe a statistical framework, termed nested partially-latent class models (npLCM), to estimate the population fraction of individuals in each class, and to predict an individual's health state given multivariate binary measurements from case-control studies. We assume each observation is a draw from a mixture model whose components represent latent health state classes. Conditional dependence among the binary measurements on an individual is induced by nesting subclasses within each latent health/disease class. Measurement precision and dependence among measurements can be estimated using the control sample for whom the class is known. Model estimation, model checking, and individual diagnosis are carried out using posterior samples drawn by Gibbs Sampler. We illustrate the model using a subset of data from the motivating Pneumonia Etiology Research for Child Health (PERCH) study that examines the distribution of pneumonia-causing bacterial or viral pathogens in developing countries.
The second part of this thesis focuses on improving the efficiency of estimating the effect of individualized intervention using data from matched-pair cluster randomized (MPCR) designs, where person-level or cluster-level covariates are available. Covariate imbalances between pairs are commonly observed under MPCR even after matching. We show that the naive approaches that ignore such imbalance are biased. We propose a covariate-calibrated approach to achieve both consistency and greater efficiency. We use the new method to evaluate the effect of an individualized health care intervention in the Guided Care study.
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Individualized health, Etiology, Diagnosis, Intervention evaluation