baker is designed for disease etiology studies from case-control data
with multiple sources of measurements with potential errors. If you are
interested in estimating the population etiology pie (a vector of fractions
that sum to one), and the probability of each cause for a particular
individual case, try baker.
Details
baker implements hierarchical Bayesian models to infer disease etiology
for multivariate binary data. We created baker to catalyze effective
communications between analysts and practicing clinicians that are vital to
the success of etiology studies. The baker package offers
modules to
Import and tidy the PERCH data (the study that motivates the creation of this package),
Transform, explore the data,
Specify, automatically generate the model files, and fit the models (npLCM),
Store and visualize posterior summaries for communicating scientific findings, and
Check and compare the fitted models.
baker has implemented models for dependent
measurements given disease status, regression analyses of etiology,
multiple imperfect measurements, different priors for true positive rates
among cases with differential measurement characteristics, and
multiple-pathogen etiology. Scientists in Pneumonia Etiology Research for
Child Health (PERCH) study usually refer to the etiology distribution
as "population etiology pie" and "individual etiology pie" for their
compositional nature, hence the name of the package (baking the pie).
See also
https://github.com/zhenkewu/baker for the source code and system/software requirements to use
bakerfor your data.