Package: rbmiUtils 0.3.0.9000

Mark Baillie

rbmiUtils: Utility Functions to Support and Extend the 'rbmi' Package

Provides utility functions that extend the capabilities of the reference-based multiple imputation package 'rbmi'. It supports clinical trial analysis workflows with functions for managing imputed datasets, applying analysis methods across imputations, and tidying results for reporting.

Authors:Mark Baillie [aut, cre, cph], Tobias Muetze [aut], Jack Talboys [aut], Lukas A. Widmer [ctb]

rbmiUtils_0.3.0.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rbmiUtils/json (API)

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

Bug tracker:https://github.com/openpharma/rbmiutils/issues

Datasets:
  • ADEFF - Example efficacy trial dataset
  • ADMI - Example multiple imputation trial dataset

On CRAN:

Conda:

multiple-imputationrbmiutils

6.04 score 4 stars 23 scripts 210 downloads 24 exports 54 dependencies

Last updated from:f9d149679e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK246
source / vignettesOK351
linux-release-x86_64OK313
macos-release-arm64OK204
macos-oldrel-arm64OK229
windows-develOK252
windows-releaseOK232
windows-oldrelOK264
wasm-releaseOK140

Exports:analyse_mi_datacombine_resultscreate_impiddescribe_drawsdescribe_imputationefficacy_tableexpand_imputed_dataextract_lsmextract_trt_effectsformat_estimateformat_pvalueformat_resultsformat_results_tablegcomp_binarygcomp_respondergcomp_responder_multiget_imputed_dataplot_forestpool_to_ardprepare_data_icereduce_imputed_datasummarise_missingnesstidy_pool_objvalidate_data

Dependencies:assertthatbackportsbeecabriocallrcheckmateclicpp11crayondescdiffobjdplyrevaluatefsgenericsgluejinjarjsonlitelatticelifecyclemagrittrMASSMatrixmmrmnlmepillarpkgbuildpkgconfigpkgloadpraiseprocessxpspurrrR6rbibutilsrbmiRcppRcppEigenRdpackrlangrprojrootsandwichstringistringrtestthattibbletidyrtidyselectTMButf8vctrswaldowithrzoo

Data Preparation and Validation
Introduction | Setup | Example Data | Define Variables | Validating Data | Catching Validation Errors | Summarising Missing Data | Missing by Visit | Subject Patterns | Summary by Treatment Group | Preparing ICE Data | Complete Workflow | Summary
Rendered fromdata-preparation.Rmdusingknitr::rmarkdown

Last update: 2026-02-16
Started: 2026-01-25

Deriving Endpoints from Imputed Data
Introduction | Prerequisites and Setup | Threshold-Based Responder (CHG > 3) | Analyse | Pool | Results | Clinical Cutoff Responder (CHG > 5) | Derive the New Endpoint | Pool and Display | Storing Results as ARD | Caveats
Rendered fromderiving-endpoints.Rmdusingknitr::rmarkdown

Last update: 2026-02-15
Started: 2026-02-15

From rbmi Analysis to Regulatory Tables
Introduction | Setup and Data | Data Preparation | Validation | Missingness Summary | rbmi Analysis Pipeline | Specify the Imputation Method | Fit the Imputation Model | Generate Imputed Datasets | Analyse Each Imputed Dataset | Pool Results | Tidying Results | Efficacy Table | Forest Plot | Treatment Difference Mode | LS Mean Display Mode | Binary/Responder Analysis
Rendered frompipeline.Rmdusingknitr::rmarkdown

Last update: 2026-02-15
Started: 2026-02-13

Storing and Analyzing Imputed Data with rbmiUtils
Introduction | Statistical Context | Step 1: Setup and Data Preparation | Step 2: Define Imputation Model | Step 3: Add Responder Variables | Step 4: Continuous Endpoint Analysis (CHG) | Step 5: Responder Endpoint Analysis (CRIT1FLN) | Define Analysis Function | Define Variables and Run Analysis | Final Notes | Efficient Storage | See Also
Rendered fromanalyse2.Rmdusingknitr::rmarkdown

Last update: 2026-02-15
Started: 2025-05-16

Efficient Storage of Imputed Data
Introduction | The Storage Problem | Setup | Example with Package Data | Reducing Imputed Data | What's in the Reduced Data? | Expanding Back to Full Data | Verifying Data Integrity | Practical Workflow | Save Reduced Data | Load and Analyse | Storage Comparison | When to Use This Approach | Edge Cases | No Missing Data | Single Imputation | Summary
Rendered fromefficient-storage.Rmdusingknitr::rmarkdown

Last update: 2026-02-13
Started: 2026-01-25

MI Diagnostics and Pipeline Inspection
Introduction | Setup | Inspecting Draws with describe_draws() | Inspecting Imputations with describe_imputation() | MI Diagnostic Statistics in ARD | When Diagnostics Are Not Available | Learn More
Rendered fromdiagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-02-13
Started: 2026-02-13

Readme and manuals

Help Manual

Help pageTopics
Example efficacy trial datasetADEFF
Example multiple imputation trial datasetADMI
Apply Analysis Function to Multiple Imputed Datasetsanalyse_mi_data
Combine Results Across Multiple Analysescombine_results
Create IMPID Column for Imputed Datasetscreate_impid
Describe an rbmi Draws Objectdescribe_draws
Describe an rbmi Imputation Objectdescribe_imputation
Create Regulatory-Style Efficacy Summary Tableefficacy_table
Expand Reduced Imputed Data to Full Datasetexpand_imputed_data
Extract Least Squares Meansextract_lsm
Extract Treatment Effect Estimatesextract_trt_effects
Format Estimate with Confidence Intervalformat_estimate
Format P-values for Publicationformat_pvalue
Format Results for Reportingformat_results
Format Results Table for Publicationformat_results_table
Utility function for Generalized G-computation for Binary Outcomesgcomp_binary
G-computation Analysis for a Single Visitgcomp_responder
G-computation for a Binary Outcome at Multiple Visitsgcomp_responder_multi
Get Imputed Data Sets as a data frameget_imputed_data
Create a Forest Plot from an rbmi Pool Objectplot_forest
Convert Pool Object to ARD Formatpool_to_ard
Prepare Intercurrent Event Dataprepare_data_ice
Print Method for Analysis Objectsprint.analysis
Print Method for describe_draws Objectsprint.describe_draws
Print Method for describe_imputation Objectsprint.describe_imputation
Print Method for Pool Objectsprint.pool
Reduce Imputed Data for Efficient Storagereduce_imputed_data
Summarise Missing Data Patternssummarise_missingness
Summary Method for Analysis Objectssummary.analysis
Summary Method for Pool Objectssummary.pool
Tidy and Annotate a Pooled Object for Publicationtidy_pool_obj
Validate Data Before Imputationvalidate_data