Nested Partially-Latent Class Models for Dependent Binary Data; Estimating Disease Etiology

TL;DR

This paper introduces a nested partially-latent class model that accounts for residual dependence in multivariate binary data, improving disease etiology estimation from case-control studies like PERCH.

Contribution

It proposes a novel Bayesian latent class model with nested subclasses to address dependence issues, enhancing accuracy in disease cause inference.

Findings

Model reduces estimation bias compared to traditional methods.

Demonstrates improved inference on simulated and real PERCH data.

Provides a flexible Bayesian framework with model averaging.

Abstract

The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. The paper describes a latent variable model designed to infer from case-control data the etiology distribution for the population of cases, and for an individual case given his or her measurements. We assume each observation is drawn from a mixture model for which each component represents one cause or disease class. The model addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcome given disease class, hence reduces estimation bias, retains efficiency and offers more valid inference. Such "local dependence" on a single subject is induced in the model by nesting latent subclasses within each disease class. Measurement precision and covariation can be estimated using the control sample for whom the class is known. In a Bayesian framework, we use stick-breaking priors on the subclass indicators for model-averaged inference across different numbers of subclasses. Assessment of model fit and individual diagnosis are done using posterior samples drawn by Gibbs sampling. We demonstrate the utility of the method on simulated and on the motivating PERCH data.