Introduction | Model robust standardization | Data Structure and Description | Syntax | Illustrative example with PPACT data sets | Illustrative Example | Step 1: Prepare the Treatment Assignment Probabilities | Step 2: Fit the MRStdCRT_fit Model | Inverse-cluster-size weighted outcome models within MRS | Fitting weights and supported models | Marginal predictions and inference | Ordinary weighted-MRS examples | Optional log-scale inference for RR and OR | A specific GEE comparison with cluster-size saturated g-computation | References
This package is considered a duplicate. The official version of this package is found at:https://deckardt98.r-universe.dev/MRStdCRT
Package: MRStdCRT 0.1.3
Changjun Li
MRStdCRT: Model-Robust Standardization in Cluster-Randomized Trials
Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>. Inverse-cluster-size weighted outcome models are available for generalized estimating equations and linear or generalized linear mixed models. The outcome models can use common fitting weights for both estimands or inverse-cluster-size weights for the cluster-average effect and no weights for the individual-average effect. Inference retains the original standardization framework and paired cluster jackknife. Related cluster-size saturated models are described in the arXiv preprint by Lee, Harhay, and Li (2026) <doi:10.48550/arXiv.2608.28943>.
Authors:
MRStdCRT_0.1.3.tar.gz
MRStdCRT_0.1.3.zip(r-4.7-any)MRStdCRT_0.1.3.zip(r-4.6-any)
MRStdCRT_0.1.3.tar.gz(r-4.7-any)MRStdCRT_0.1.3.tar.gz(r-4.6-any)
MRStdCRT_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MRStdCRT/json (API)
| # Install 'MRStdCRT' in R: |
| install.packages('MRStdCRT', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/deckardt98/mrstdcrt/issues
Datasets:
- data_sim_binary - Example Dataset: Simulated CRT
- data_sim_continuous - Example Dataset: Simulated CRT
- ppact - Example Dataset: PPACT
Last updated from:54265c8780. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 230 | ||
| source / vignettes | OK | 240 | ||
| linux-release | OK | 277 | ||
| windows-devel | OK | 171 | ||
| windows-release | OK | 264 | ||
| wasm-release | OK | 100 |
Exports:MRStdCRT_fit
Dependencies:backportsbootbroomclicpp11dplyrgeepackgenericsgluelatticelifecyclelme4magrittrMASSMatrixmatrixStatsminqanlmenloptrnumDerivpillarpkgconfigpurrrR6rbibutilsRcppRcppEigenRdpackreformulasrlangstringistringrtibbletidyrtidyselectutf8vctrsWeMixwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Example Dataset: Simulated CRT (binary outcome) | data_sim_binary |
| Example Dataset: Simulated CRT (continuous outcome) | data_sim_continuous |
| Model-robust Standardization Estimators for the Cluster Randomized Trials | MRStdCRT_fit |
| Example Dataset: PPACT | ppact |
| Summarize a 'MRS_obj' Fit | summary.MRS_obj |