Package: twoStageDesignTMLE 1.0
twoStageDesignTMLE: Targeted Maximum Likelihood Estimation for Two-Stage Study Design
An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.
Authors:
twoStageDesignTMLE_1.0.tar.gz
twoStageDesignTMLE_1.0.zip(r-4.5)twoStageDesignTMLE_1.0.zip(r-4.4)twoStageDesignTMLE_1.0.zip(r-4.3)
twoStageDesignTMLE_1.0.tgz(r-4.4-any)twoStageDesignTMLE_1.0.tgz(r-4.3-any)
twoStageDesignTMLE_1.0.tar.gz(r-4.5-noble)twoStageDesignTMLE_1.0.tar.gz(r-4.4-noble)
twoStageDesignTMLE_1.0.tgz(r-4.4-emscripten)twoStageDesignTMLE_1.0.tgz(r-4.3-emscripten)
twoStageDesignTMLE.pdf |twoStageDesignTMLE.html✨
twoStageDesignTMLE/json (API)
NEWS
| # Install 'twoStageDesignTMLE' in R: |
| install.packages('twoStageDesignTMLE', repos = c('https://sg-tlr.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:e87d2870ef. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 06 2024 |
| R-4.5-win | OK | Nov 06 2024 |
| R-4.5-linux | OK | Nov 06 2024 |
| R-4.4-win | OK | Nov 06 2024 |
| R-4.4-mac | OK | Nov 06 2024 |
| R-4.3-win | OK | Nov 06 2024 |
| R-4.3-mac | OK | Nov 06 2024 |
Exports:evalAugWsetVtwoStageDesignTMLENewstwoStageTMLEtwoStageTMLEmsm
Dependencies:bitopscaToolscodetoolscvAUCdata.tableforeachgamglmnetgplotsgtoolsiteratorsKernSmoothlatticeMatrixnnlsRcppRcppEigenROCRshapeSuperLearnersurvivaltmle
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| .evalAugW calls TMLE to use super learner to evalute preliminary predictions for Q(0,W) and Q(1,W) conditioning on stage 1 covariates | evalAugW |
| print.summary.twoStageTMLE | print.summary.twoStageTMLE |
| print.twoStageTMLE | print.twoStageTMLE |
| Utilities setV Set the number of cross-validation folds as a function of effective sample size See Phillips 2023 doi.org/10.1093/ije/dyad023 | setV |
| summary.twoStageTMLE | summary.twoStage |
| summary.twoStageTMLE | summary.twoStageTMLE |
| twoStageDesignTMLENews Get news about recent updates and bug fixes | twoStageDesignTMLENews |
| twoStageTMLE | twoStageTMLE |
| twoStageTMLEmsm | twoStageTMLEmsm |
