LocalControl: Nonparametric Methods for Generating High Quality Comparative Effectiveness Evidence

Implements novel nonparametric approaches to address biases and confounding when comparing treatments or exposures in observational studies of outcomes. While designed and appropriate for use in studies involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. The package implements a family of methods for non-parametric bias correction when comparing treatments in observational studies, including survival analysis settings, where competing risks and/or censoring may be present. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups.

Package details

AuthorNicolas R. Lauve [aut], Stuart J. Nelson [aut], S. Stanley Young [aut], Robert L. Obenchain [aut], Melania Pintilie [ctb], Martin Kutz [ctb], Christophe G. Lambert [aut, cre]
MaintainerChristophe G. Lambert <cglambert@unm.edu>
LicenseApache License 2.0 | file LICENSE
Package repositoryView on CRAN
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LocalControl documentation built on April 14, 2020, 6:17 p.m.