Package: CIMTx 1.2.0

CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome

Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>.

Authors:Liangyuan Hu [aut], Chenyang Gu [aut], Michael Lopez [aut], Jiayi Ji [aut, cre]

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CIMTx.pdf |CIMTx.html✨
CIMTx/json (API)

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

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.43 score 27 scripts 271 downloads 4 exports 110 dependencies

Last updated 2 years agofrom:2bb67b80b8. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-winNOTENov 13 2024
R-4.5-linuxNOTENov 13 2024
R-4.4-winNOTENov 13 2024
R-4.4-macNOTENov 13 2024
R-4.3-winNOTENov 13 2024
R-4.3-macNOTENov 13 2024

Exports:ce_estimatedata_simsatrue_c_fun_cal

Dependencies:abindarmbackportsBARTbitopsbootcachemcaToolscheckmatechkclassclassIntclicobaltcodacodetoolscolorspacecowplotcpp11crayoncvAUCdata.tableDBIdeldirdigestdoParalleldplyre1071fansifarverfastmapforeachFormulaformula.toolsgamgbmgenericsggplot2glmnetgluegplotsgridExtragtablegtoolsinterpisobanditeratorsjpegjsonliteKernSmoothlabelinglatticelatticeExtralifecyclelme4lubridatemagrittrMASSMatchingMatrixMatrixModelsmemoisemetRmgcvminqamitoolsmunsellnlmenloptrnnetnnlsnumDerivoperator.toolspillarpkgconfigplyrpngproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangROCRs2scalessfshapestringistringrSuperLearnersurveysurvivaltibbletidyrtidyselecttimechangetmletwangunitsutf8vctrsviridisLiteWeightItwithrwkxgboostxtable

Readme and manuals

Help Manual

Help pageTopics
Causal inference with multiple treatments using observational datace_estimate
Causal inference with multiple treatments using BART for ATE effectsce_estimate_bart_ate
Causal inference with multiple treatments using BART for ATT effectsce_estimate_bart_att
Causal inference with multiple treatments using IPTW for ATE effectsce_estimate_iptw_ate
Causal inference with multiple treatments using IPTW for ATE effects (bootstrapping for CI)ce_estimate_iptw_ate_boot
Causal inference with multiple treatments using IPTW for ATT effectsce_estimate_iptw_att
Causal inference with multiple treatments using IPTW for ATT effects (bootstrapping for CI)ce_estimate_iptw_att_boot
Causal inference with multiple treatments using RA for ATE effectsce_estimate_ra_ate
Causal inference with multiple treatments using RA for ATT effectsce_estimate_ra_att
Causal inference with multiple treatments using RAMS for ATE effectsce_estimate_rams_ate
Causal inference with multiple treatments using RAMS for ATE effects (bootstrapping for CI)ce_estimate_rams_ate_boot
Causal inference with multiple treatments using RAMS for ATT effectsce_estimate_rams_att
Causal inference with multiple treatments using RAMS for ATT effects (bootstrapping for CI)ce_estimate_rams_att_boot
Causal inference with multiple treatments using TMLE for ATE effectsce_estimate_tmle_ate
Causal inference with multiple treatments using TMLE for ATE effects (bootstrapping for CI)ce_estimate_tmle_ate_boot
Causal inference with multiple treatments using VM for ATT effectsce_estimate_vm_att
Covariate overlap figurecovariate_overlap
Simulate data for binary outcome with multiple treatmentsdata_sim
Plot for non-IPTW estimation methods with bootstrapping for ATE effectplot.CIMTx_ATE_posterior
Plot for non-IPTW estimation methods for ATT effectplot.CIMTx_ATT_posterior
Boxplot for weight distributionplot.CIMTx_IPTW
Plot for non-IPTW estimation methods for ATE effectplot.CIMTx_nonIPTW_once
Contour plot for the grid specification of sensitivity analysisplot.CIMTx_sa_grid
Posterior distribution summaryposterior_summary
Print the ATE results for non-IPTW resultsprint.CIMTx_ATE_posterior
Print the ATE results for from sensitivity analysisprint.CIMTx_ATE_sa
Print the ATT resultsprint.CIMTx_ATT_posterior
Print the ATT results for from sensitivity analysisprint.CIMTx_ATT_sa
Print the ATE/ATT results for IPTW resultsprint.CIMTx_IPTW
Print the ATE/ATT results for non-IPTW resultsprint.CIMTx_nonIPTW_once
Print the ATT results for from sensitivity analysisprint.CIMTx_sa_grid
Flexible Monte Carlo sensitivity analysis for unmeasured confoundingsa
Summarize a CIMTx_ATE_posterior objectsummary.CIMTx_ATE_posterior
Summarize a CIMTx_ATE_sa objectsummary.CIMTx_ATE_sa
Summarize a CIMTx_ATT_posterior objectsummary.CIMTx_ATT_posterior
Summarize a CIMTx_ATT_sa objectsummary.CIMTx_ATT_sa
Summarize a CIMTx_IPTW objectsummary.CIMTx_IPTW
Summarize a CIMTx_nonIPTW_once objectsummary.CIMTx_nonIPTW_once
Calculate the true c functions with 3 treatments and a binary predictortrue_c_fun_cal
Trimmingtrunc_fun