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precmed

Precision Medicine - Comprehensive R package

A doubly robust precision medicine approach to estimate and validate conditional average treatment effects

Installation

The precmed package can be installed from CRAN as follows:

install.packages("precmed")

The latest version can be installed from GitHub as follows:

install.packages("devtools")
devtools::install_github(repo = "smartdata-analysis-and-statistics/precmed")

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Version

Install

install.packages('precmed')

Monthly Downloads

179

Version

1.0.0

License

Apache License (== 2.0)

Maintainer

Thomas Debray

Last Published

October 12th, 2022

Functions in precmed (1.0.0)

Doubly robust estimator of and inference for the average treatment effect for continuous data

Doubly robust estimator of and inference for the average treatment effect for survival data

arg.checks.common

Check arguments that are common to all types of outcome USed inside arg.checks()

Compute the area between curves from the "precmed" object

Compute the area between curves from the "precmed" object

Split the given dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

Estimation of the conditional average treatment effect (CATE) score for count data

Cross-validation of the conditional average treatment effect (CATE) score for count, survival or continuous outcomes

Cross-validation of the conditional average treatment effect (CATE) score for continuous outcomes

boxplot.precmed

A set of box plots of estimated ATEs from the "precmed" object

Estimation of the conditional average treatment effect (CATE) score for survival data

Cross-validation of the conditional average treatment effect (CATE) score for count outcomes

balancesurv.split

Split the given time-to-event dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

Cross-validation of the conditional average treatment effect (CATE) score for survival outcomes

Estimation of the conditional average treatment effect (CATE) score for continuous data

Estimation of the conditional average treatment effect (CATE) score for count, survival and continuous data

data.preproc.surv

Data preprocessing Apply at the beginning of catefitcount(), catecvcount(), catefitsurv(), and catecvsurv(), after arg.checks()

Simulated data with count outcome

Doubly robust estimator of the average treatment effect for count data

Doubly robust estimator of the average treatment effect for continuous data

estcount.multilevel.subgroup

Estimate the ATE of the log RR ratio in one multilevel subgroup defined by the proportions

Doubly robust estimator of the average treatment effect with Cox model for survival data

estcount.bilevel.subgroups

Estimate the Average Treatment Effect of the log risk ratio in multiple bi-level subgroups defined by the proportions

data.preproc.mean

Data preprocessing Apply at the beginning of catefitmean() and catecvmean(), after arg.checks()

Estimate restricted mean survival time (RMST) based on Cox regression model

Data preprocessing Apply at the beginning of pmcount() and cvcount(), after arg.checks()

Estimate the CATE model using specified scoring methods for survival outcomes

estmean.bilevel.subgroups

Estimate the ATE of the mean difference in multiple bi-level subgroups defined by the proportions

Propensity score estimation with LASSO

Estimate the CATE model using specified scoring methods

glm.simplereg.ps

Propensity score estimation with a linear model

estmean.multilevel.subgroup

Estimate the ATE of the mean difference in one multilevel subgroup defined by the proportions

estsurv.multilevel.subgroups

Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in one multilevel subgroup defined by the proportions

estsurv.bilevel.subgroups

Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in multiple bi-level subgroups defined by the proportions

Estimate the CATE model using specified scoring methods

Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods

Print function for atefit

onearmglmcount.dr

Doubly robust estimators of the coefficients in the two regression

onearmglmmean.dr

Doubly robust estimators of the coefficients in the two regression

Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information

survivalExample

Simulated data with survival outcome

Doubly robust estimators of the coefficients in the two regression

Histogram of bootstrap estimates

Catch errors and warnings when estimating the ATEs in the nested subgroup

Two side-by-side line plots of validation curves from the "precmed" object

Print function for atefit

twoarmglmmean.dr

Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix

Probability of being censored

twoarmglmcount.dr

Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information

Catch errors and warnings when estimating the ATEs in the nested subgroup for continuous data

Simulated data with a continuous outcome

Calculate the CATE score given the baseline covariates for specified scoring method methods

Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods for survival outcomes

Doubly robust estimator of and inference for the average treatment effect for count, survival and continuous data

Check arguments Catered to all types of outcome Apply at the beginning of pmcount(), cvcount(), drcount.inference(), catefitsurv(), catecvsurv(), and drsurv.inference()

balancemean.split

Split the given dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

Doubly robust estimator of and inference for the average treatment effect for count data