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 nonparametric bias correction when comparing treatments in cross-sectional, case-control, and survival analysis settings, including competing risks with censoring. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups.
|License||Apache License 2.0 | file LICENSE|
|Package repository||View on GitHub|
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