impart – An R Package for Randomized Trials with Covariate Adjustment or Information Monitoring

Music City Center 

Covariate adjustment in randomized trials remains underutilized despite its potential to improve precision while requiring the same or weaker assumptions for validity compared to an unadjusted analysis. Such methods may not be directly compatible with group sequential designs (GSDs), which are commonly used for pre-planned interim analyses.
'impart' is an R package that allows users to design, monitor, and analyze randomized trials which can incorporate both covariate adjustment and pre-planned interim analyses. The package includes functions for pre-trial planning, monitoring ongoing studies, and performing analyses. Functions facilitate planning trials with continuous, binary, and ordinal outcomes. Monitoring and analyses can be done using pre-specified methods (e.g., G-computation), or user-supplied functions. Variance estimates are computed using the nonparametric bootstrap and can be orthogonalized to enforce the independent increments assumption necessary to utilize GSD stopping boundaries. 'impart' also facilitates information monitoring (i.e. continuous sample size re-estimation), allowing recruitment to be adapted to accruing data, avoiding under- or overpowered trials.

Randomized Trials

Covariate Adjustment

Adaptive Designs

Sample Size Re-estimation

Information Monitoring

Group Sequential Designs 

Biopharmaceutical Section