⚠️There's a newer version (0.2-3) of this package.Take me there.

adapt4pv (version 0.2-2)

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. See Courtois et al .

Copy Link

Version

Install

install.packages('adapt4pv')

Monthly Downloads

311

Version

0.2-2

License

GPL-2

Maintainer

Emeline Courtois

Last Published

May 30th, 2022

Functions in adapt4pv (0.2-2)

adapt4pv-package

Adaptive approaches for signal detection in PharmacoVigilance

adapt_cisl

fit an adaptive lasso with adaptive weights derived from CISL

est_ps_hdps

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

data_PV

Simulated data for the adapt4pv package

adapt_univ

fit an adaptive lasso with adaptive weights derived from univariate coefficients

est_ps_xgb

propensity score estimation in high dimension using gradient tree boosting

adapt_cv

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

est_ps_bic

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

adapt_bic

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

cisl

Class Imbalanced Subsampling Lasso

lasso_bic

fit a lasso regression and use standard BIC for variable selection

ps_adjust

adjustment on propensity score

lasso_perm

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

ps_pond_one

weihting on propensity score for one drug exposure

lasso_cv

wrap function for cv.glmnet

summary_stat

Summary statistics for main adapt4pv package functions

ps_pond

weihting on propensity score

ps_adjust_one

adjustment on propensity score for one drug exposure