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adapt4pv (version 0.1.0)

Adaptive Approaches for Signal Detection in Pharmacovigilance

Description

A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied.

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Version

Install

install.packages('adapt4pv')

Monthly Downloads

261

Version

0.1.0

License

GPL-2

Maintainer

Emeline Courtois

Last Published

June 24th, 2020

Functions in adapt4pv (0.1.0)

adapt_cisl

fit an adaptive lasso with adaptive weights derived from CISL

cisl

Class Imbalanced Subsampling Lasso

est_ps_hdps

propensity score estimation in high dimension with automated covariates selection using hdPS

adapt4pv-package

Adaptive approaches for signal detection in PharmacoVigilance

adapt_ridge

fit an adaptive lasso with adaptive weights derived from ridge regression

est_ps_bic

propensity score estimation in high dimension with automated covariates selection using lasso-bic

adapt_cv

fit an adaptive lasso with adaptive weights derived from lasso-cv

data_PV

Simulated data for the adapt4pv package

adapt_bic

fit an adaptive lasso with adaptive weights derived from lasso-bic

adapt_univ

fit an adaptive lasso with adaptive weights derived from univariate coefficients

lasso_cv

wrap function for cv.glmnet

ps_adjust_one

adjustment on propensity score for one drug exposure

ps_adjust

adjustment on propensity score

summary_stat

Summary statistics for main adapt4pv package functions

lasso_bic

fit a lasso regression and use standard BIC for variable selection

est_ps_xgb

propensity score estimation in high dimension using gradient tree boosting

ps_pond_one

weihting on propensity score for one drug exposure

ps_pond

weihting on propensity score

lasso_perm

fit a lasso regression and use standard permutation of the outcome for variable selection