Package: precmed 1.1.0.9000

Thomas Debray

precmed: Precision Medicine

A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.

Authors:Lu Tian [aut], Xiaotong Jiang [aut], Gabrielle Simoneau [aut], Biogen MA Inc. [cph], Thomas Debray [ctb, cre], Stan Wijn [ctb], Joana Caldas [ctb]

precmed_1.1.0.9000.tar.gz
precmed_1.1.0.9000.zip(r-4.7)precmed_1.1.0.9000.zip(r-4.6)precmed_1.1.0.9000.zip(r-4.5)
precmed_1.1.0.9000.tgz(r-4.6-any)precmed_1.1.0.9000.tgz(r-4.5-any)
precmed_1.1.0.9000.tar.gz(r-4.7-any)precmed_1.1.0.9000.tar.gz(r-4.6-any)
precmed_1.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
precmed/json (API)

# Install 'precmed' in R:

install.packages('precmed', repos = c('https://smartdata-analysis-and-statistics.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/smartdata-analysis-and-statistics/precmed/issues

Pkgdown/docs site:https://smartdata-analysis-and-statistics.github.io

Datasets:

countExample - Simulated data with count outcome
meanExample - Simulated data with a continuous outcome
survivalExample - Simulated data with survival outcome

On CRAN:

precision-medicine

4.08 score 6 stars 6 scripts 238 downloads 11 exports 82 dependencies

Last updated from:d13cd47476. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	176
source / vignettes	OK	184
linux-release-x86_64	OK	178
macos-release-arm64	OK	100
macos-oldrel-arm64	OK	95
windows-devel	OK	127
windows-release	OK	162
windows-oldrel	OK	134
wasm-release	OK	131

Exports:abc atefit atefitcount atefitsurv auc catecv catecvcount catecvsurv catefit catefitcount catefitsurv

Dependencies:base64enc bit bit64 bslib cachem cli clipr codetools cpp11 crayon data.tree DiagrammeR digest dplyr evaluate farver fastmap fontawesome foreach fs gam gbm generics ggplot2 glmnet glue gtable highr hms htmltools htmlwidgets igraph isoband iterators jquerylib jsonlite knitr labeling lattice lifecycle magrittr MASS Matrix memoise mgcv mime nlme pillar pkgconfig prettyunits progress purrr R6 randomForestSRC rappdirs RColorBrewer Rcpp RcppEigen readr rlang rmarkdown rstudioapi S7 sass scales shape stringi stringr survival tibble tidyr tidyselect tinytex tzdb utf8 vctrs viridisLite visNetwork vroom withr xfun yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute the area between curves from the '"precmed"' object	abc abc.default
Compute the area between curves from the '"precmed"' object	abc.precmed
Check arguments Catered to all types of outcome Apply at the beginning of 'pmcount()', 'cvcount()', 'drcount.inference()', 'catefitsurv()', 'catecvsurv()', and 'drsurv.inference()'	arg.checks
Check arguments that are common to all types of outcome USed inside 'arg.checks()'	arg.checks.common
Doubly robust estimator of and inference for the average treatment effect for count, survival and continuous data	atefit
Doubly robust estimator of and inference for the average treatment effect for count data	atefitcount
Doubly robust estimator of and inference for the average treatment effect for continuous data	atefitmean
Doubly robust estimator of and inference for the average treatment effect for survival data	atefitsurv
Compute the area under the curve using linear or natural spline interpolation	auc
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	balance.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	balancemean.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	balancesurv.split
A set of box plots of estimated ATEs from the '"precmed"' object	boxplot.precmed
Cross-validation of the conditional average treatment effect (CATE) score for count, survival or continuous outcomes	catecv
Cross-validation of the conditional average treatment effect (CATE) score for count outcomes	catecvcount
Cross-validation of the conditional average treatment effect (CATE) score for continuous outcomes	catecvmean
Cross-validation of the conditional average treatment effect (CATE) score for survival outcomes	catecvsurv
Estimation of the conditional average treatment effect (CATE) score for count, survival and continuous data	catefit
Estimation of the conditional average treatment effect (CATE) score for count data	catefitcount
Estimation of the conditional average treatment effect (CATE) score for continuous data	catefitmean
Estimation of the conditional average treatment effect (CATE) score for survival data	catefitsurv
Simulated data with count outcome	countExample
Estimate restricted mean survival time (RMST) based on Cox regression model	cox.rmst
Data preprocessing Apply at the beginning of 'pmcount()' and 'cvcount()', after 'arg.checks()'	data.preproc
Data preprocessing Apply at the beginning of 'catefitmean()' and 'catecvmean()', after 'arg.checks()'	data.preproc.mean
Data preprocessing Apply at the beginning of 'catefitcount()', 'catecvcount()', 'catefitsurv()', and 'catecvsurv()', after 'arg.checks()'	data.preproc.surv
Doubly robust estimator of the average treatment effect for count data	drcount
Doubly robust estimator of the average treatment effect for continuous data	drmean
Doubly robust estimator of the average treatment effect with Cox model for survival data	drsurv
Estimate the Average Treatment Effect of the log risk ratio in multiple bi-level subgroups defined by the proportions	estcount.bilevel.subgroups
Estimate the ATE of the log RR ratio in one multilevel subgroup defined by the proportions	estcount.multilevel.subgroup
Estimate the ATE of the mean difference in multiple bi-level subgroups defined by the proportions	estmean.bilevel.subgroups
Estimate the ATE of the mean difference in one multilevel subgroup defined by the proportions	estmean.multilevel.subgroup
Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in multiple bi-level subgroups defined by the proportions	estsurv.bilevel.subgroups
Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in one multilevel subgroup defined by the proportions	estsurv.multilevel.subgroups
Generate K-fold Indices for Cross-Validation	generate_kfold_indices
Propensity score estimation with LASSO	glm.ps
Propensity score estimation with a linear model	glm.simplereg.ps
Estimate the CATE model using specified scoring methods	intxcount
Estimate the CATE model using specified scoring methods	intxmean
Estimate the CATE model using specified scoring methods for survival outcomes	intxsurv
Probability of being censored	ipcw.surv
Catch errors and warnings when estimating the ATEs in the nested subgroup for continuous data	meanCatch
Simulated data with a continuous outcome	meanExample
Doubly robust estimators of the coefficients in the two regression	onearmglmcount.dr
Doubly robust estimators of the coefficients in the two regression	onearmglmmean.dr
Doubly robust estimators of the coefficients in the two regression	onearmsurv.dr
Histogram of bootstrap estimates	plot.atefit
Two side-by-side line plots of validation curves from the '"precmed"' object	plot.precmed
Print function for atefit	print.atefit
Print function for atefit	print.catefit
Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods	scorecount
Calculate the CATE score given the baseline covariates for specified scoring method methods	scoremean
Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods for survival outcomes	scoresurv
Catch errors and warnings when estimating the ATEs in the nested subgroup	survCatch
Simulated data with survival outcome	survivalExample
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information	twoarmglmcount.dr
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix	twoarmglmmean.dr
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information	twoarmsurv.dr