mvMISE

A General Framework for Multivariate Mixed-Effects Selection Models with Potential Missing Data

This R package offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) doi:10.1093/biostatistics/kxy022. The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models:

1. mvMISE_b allows correlated outcome-specific random intercepts with a factor-analytic structure;
2. mvMISE_e allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix.

Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness.

Installation

• For the stable version from CRAN:

install.packages('mvMISE')

• For the development version (requiring the devtools package):

# devtools::install_github('randel/mvMISE')
install.packages("mvMISE", repos = c('https://randel.r-universe.dev', 'https://cloud.r-project.org'))

Reference

Wang, J., Wang, P., Hedeker, D., & Chen, L. S. (2018). Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness. Biostatistics.