Package: precmed 1.1.0

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.tar.gz
precmed_1.1.0.tar.gz(r-4.5-noble)precmed_1.1.0.tar.gz(r-4.4-noble)
precmed_1.1.0.tgz(r-4.4-emscripten)precmed_1.1.0.tgz(r-4.3-emscripten)
precmed.pdf |precmed.html✨
precmed/json (API)
NEWS

# Install 'precmed' in R:
install.packages('precmed', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

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

Datasets:

1.70 score 1 stars 4 scripts 171 downloads 11 exports 84 dependencies

Last updated 3 months agofrom:b8682040df. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 05 2024
R-4.5-linuxOKDec 05 2024

Exports:abcatefitatefitcountatefitsurvauccatecvcatecvcountcatecvsurvcatefitcatefitcountcatefitsurv

Dependencies:base64encbitbit64bslibcachemclicliprcodetoolscolorspacecpp11crayondata.treeDiagrammeRdigestdplyrevaluatefansifarverfastmapfontawesomeforeachfsgamgbmgenericsggplot2glmnetgluegtablehighrhmshtmltoolshtmlwidgetsigraphisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigprettyunitsprogresspurrrR6randomForestSRCrappdirsRColorBrewerRcppRcppEigenreadrrlangrmarkdownrstudioapisassscalesshapestringistringrsurvivaltibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Compute the area between curves from the '"precmed"' objectabc abc.default
Compute the area between curves from the '"precmed"' objectabc.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 dataatefit
Doubly robust estimator of and inference for the average treatment effect for count dataatefitcount
Doubly robust estimator of and inference for the average treatment effect for continuous dataatefitmean
Doubly robust estimator of and inference for the average treatment effect for survival dataatefitsurv
Compute the area under the curve using linear or natural spline interpolationauc
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 setsbalance.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 setsbalancemean.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 setsbalancesurv.split
A set of box plots of estimated ATEs from the '"precmed"' objectboxplot.precmed
Cross-validation of the conditional average treatment effect (CATE) score for count, survival or continuous outcomescatecv
Cross-validation of the conditional average treatment effect (CATE) score for count outcomescatecvcount
Cross-validation of the conditional average treatment effect (CATE) score for continuous outcomescatecvmean
Cross-validation of the conditional average treatment effect (CATE) score for survival outcomescatecvsurv
Estimation of the conditional average treatment effect (CATE) score for count, survival and continuous datacatefit
Estimation of the conditional average treatment effect (CATE) score for count datacatefitcount
Estimation of the conditional average treatment effect (CATE) score for continuous datacatefitmean
Estimation of the conditional average treatment effect (CATE) score for survival datacatefitsurv
Simulated data with count outcomecountExample
Estimate restricted mean survival time (RMST) based on Cox regression modelcox.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 datadrcount
Doubly robust estimator of the average treatment effect for continuous datadrmean
Doubly robust estimator of the average treatment effect with Cox model for survival datadrsurv
Estimate the Average Treatment Effect of the log risk ratio in multiple bi-level subgroups defined by the proportionsestcount.bilevel.subgroups
Estimate the ATE of the log RR ratio in one multilevel subgroup defined by the proportionsestcount.multilevel.subgroup
Estimate the ATE of the mean difference in multiple bi-level subgroups defined by the proportionsestmean.bilevel.subgroups
Estimate the ATE of the mean difference in one multilevel subgroup defined by the proportionsestmean.multilevel.subgroup
Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in multiple bi-level subgroups defined by the proportionsestsurv.bilevel.subgroups
Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in one multilevel subgroup defined by the proportionsestsurv.multilevel.subgroups
Generate K-fold Indices for Cross-Validationgenerate_kfold_indices
Propensity score estimation with LASSOglm.ps
Propensity score estimation with a linear modelglm.simplereg.ps
Estimate the CATE model using specified scoring methodsintxcount
Estimate the CATE model using specified scoring methodsintxmean
Estimate the CATE model using specified scoring methods for survival outcomesintxsurv
Probability of being censoredipcw.surv
Catch errors and warnings when estimating the ATEs in the nested subgroup for continuous datameanCatch
Simulated data with a continuous outcomemeanExample
Doubly robust estimators of the coefficients in the two regressiononearmglmcount.dr
Doubly robust estimators of the coefficients in the two regressiononearmglmmean.dr
Doubly robust estimators of the coefficients in the two regressiononearmsurv.dr
Histogram of bootstrap estimatesplot.atefit
Two side-by-side line plots of validation curves from the '"precmed"' objectplot.precmed
Print function for atefitprint.atefit
Print function for atefitprint.catefit
Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methodsscorecount
Calculate the CATE score given the baseline covariates for specified scoring method methodsscoremean
Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods for survival outcomesscoresurv
Catch errors and warnings when estimating the ATEs in the nested subgroupsurvCatch
Simulated data with survival outcomesurvivalExample
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence informationtwoarmglmcount.dr
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrixtwoarmglmmean.dr
Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence informationtwoarmsurv.dr