Package: QTOCen 0.1.1

QTOCen: Quantile-Optimal Treatment Regimes with Censored Data

Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.

Authors:Yu Zhou [cre, aut], Lan Wang [ctb]

QTOCen_0.1.1.tar.gz
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QTOCen_0.1.1.tgz(r-4.6-any)QTOCen_0.1.1.tgz(r-4.5-any)
QTOCen_0.1.1.tar.gz(r-4.7-any)QTOCen_0.1.1.tar.gz(r-4.6-any)
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manual.pdf |manual.html✨
card.svg |card.png
QTOCen/json (API)

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

On CRAN:

Conda:

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

1.00 score 1 stars 33 downloads 15 exports 10 dependencies

Last updated from:5ed2aa6344. Checks:6 NOTE, 2 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE155
source / vignettesOK222
linux-release-x86_64NOTE154
macos-release-arm64NOTE133
macos-oldrel-arm64NOTE117
windows-develNOTE98
windows-releaseNOTE122
windows-oldrelFAIL44
wasm-releaseOK123

Exports:Bnk_funcest_mean_ipweest_quant_ipweest_quant_TwoStg_ipweGene_Mean_CenIPWEGene_Quantile_CenIPWEGene_Quantile_CenIPWE_DTRIPWE_mean_IndCenIPWE_Qopt_DepCen_generalIPWE_Qopt_DepCen_trtIPWE_Qopt_DTR_IndCenIPWE_Qopt_IndCenLocalKMsimJLSDdatatauhat_func

Dependencies:latticeMASSMatrixMatrixModelsquantregrbibutilsRdpackrgenoudSparseMsurvival

Readme and manuals

Help Manual

Help pageTopics
Generate biquadratic kernel weights for a univariate variableBnk_func
Estimate the marginal mean response of a linear static treatment regimeest_mean_ipwe
Estimate the marginal quantile response of a linear static treatment regimeest_quant_ipwe
Estimate the marginal quantile response of a specific dynamic TRest_quant_TwoStg_ipwe
A low-level function for the generic optimization step in estimating Mean-optimal treatment regime for censored dataGene_Mean_CenIPWE
A low-level function for the generic optimization step in estimating Quanilte-optimal treatment regime for censored dataGene_Quantile_CenIPWE
A low-level function for the generic optimization step in estimating dynamic Quanilte-optimal treatment regime for censored dataGene_Quantile_CenIPWE_DTR
Estimate the mean-optimal treatment regime for data with independently censored responseIPWE_mean_IndCen
Estimate Quantile-optimal Treatment Regime for covariates-dependent random censoring dataIPWE_Qopt_DepCen_general
Estimate the Quantile-opt Treatment Regime under the assumption that the censoring time's distribution only depends on treatment levelIPWE_Qopt_DepCen_trt
Function to estimate the two-stage quantile-optimal dynamic treatment regime for censored data: the independent censoring CaseIPWE_Qopt_DTR_IndCen
Function to estimate the quantile-optimal treatment regime: the independent censoring CaseIPWE_Qopt_IndCen
Kernel-based Local Kaplan-Meier EstimatorLocalKM
Function to generate simulation data from a sequentially randomized experiment designed in (Jiang et al. 2017)simJLSDdata
Kernel-based Local Kaplan-Meier Estimator for the Conditional Probability of the Survival Timetauhat_func