Tools for Designing, Simulating, and Analyzing Implementation Rollout Trials
Create a binary outcome from linear predictors
Add an error term for simulation
Add a fixed effect column for simulation
Create a linear outcome by summing effects
Expand a data frame with parameter combinations for simulation
Create a Poisson outcome from linear predictors
Add a random effect column for simulation
Compute the proportion of values within term-specific intervals within...
Compute bias relative to term-specific true values within grouped simu...
Compute the proportion of values above term-specific thresholds within...
Compute the proportion of values below term-specific thresholds within...
Compute the observed quantile value for each term within grouped simul...
Summarise simulation results from extracted model estimates
Extract and tidy model results from a column of models
Fit models in parallel across a list-column of datasets
Add replicate identifiers for simulation replicates
Join unit-level information to a long-format rollout schedule
Pivot a rollout schedule from wide to long format with local time calc...
rollout: Tools for Designing, Simulating, and Analyzing Implementation...
Provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians.
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