In population studies on the etiology of disease, one goal is the estimation
of the fraction of cases attributable to each of several causes. For example,
pneumonia is a clinical diagnosis of lung infection that may be caused by
viral, bacterial, fungal, or other pathogens. The study of pneumonia etiology
is challenging because directly sampling from the lung to identify the
etiologic pathogen is not standard clinical practice in most settings. Instead,
measurements from multiple peripheral specimens are made. This paper introduces
the statistical methodology designed for estimating the population etiology
distribution and the individual etiology probabilities in the Pneumonia
Etiology Research for Child Health (PERCH) study of 9; 500 children for 7 sites
around the world. We formulate the scientific problem in statistical terms as
estimating the mixing weights and latent class indicators under a
partially-latent class model (pLCM) that combines heterogeneous measurements
with different error rates obtained from a case-control study. We introduce the
pLCM as an extension of the latent class model. We also introduce graphical
displays of the population data and inferred latent-class frequencies. The
methods are tested with simulated data, and then applied to PERCH data. The
paper closes with a brief description of extensions of the pLCM to the
regression setting and to the case where conditional independence among the
measures is relaxed.Comment: 25 pages, 4 figures, 1 supplementary materia
