• Coley RY, Fisher AJ, Mamawala M, Carter HB, Pienta KJ, Zeger SL. "A Bayseian Hierarchical Model for Prediction of Latent Health States from Multiple Data Sources with Application to Active Surveillance of Prostate Cancer." (2016). Link.
  • Fisher AJ, Coley RY, Zeger SL. "Fast Out-of-Sample Predictions for Bayesian Hierarchical Models of Latent Health States." (2015). Link.
  • Wu Z, Deloria-Knoll M, Hammitt LL, and Zeger SL, for the PERCH Core Team (2016). “Partially Latent Class Models (pLCM) for Case-Control Studies of Childhood Pneumonia Etiology.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 65: 97-114. doi: 10.1111/rssc.12101. Link.
  • Wu Z, Deloria-Knoll M, and Zeger SL (2016+). “Nested Partially-Latent Class Models (npLCM) for Dependent Binary Data; Estimating Disease Etiology.” (Under revision). Link.

Useful R Packages

Task Package Description
Health state variable definition and estimation from diverse data types; Causal inference about treatment main effects dagR

Contains functions to draw, manipulate, and evaluate directed acyclic graphs (DAG), with a focus on epidemiologic applications, namely the assessment of adjustment sets and potentially biasing paths

Latent class and trend identification/ estimation with repeated, multivariate, diverse observations (RMD observations) poLCA

Estimation of latent class models and latent class regression models for polytomous outcome variables

Causal inference of interactions with a large number of potential effect modifiers sem

Functions for estimating structural equations in observed-variables models by two-stage least squares, and for fitting general structural equation models with multinormal errors and latent variables by full-information maximum likelihood


Causal structure learning and estimation of causal effects from observational data


Estimates and helps interpret the results of an enormous range of statistical models