Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.
doParallel, doRNG, foreach, MASS, mvtnorm, ReporteRs, stats,
survival, utils

Provides a general framework for clinical trial simulations based
on the Clinical Scenario Evaluation (CSE) approach. The package supports a
broad class of data models (including clinical trials with continuous, binary,
survival-type and count-type endpoints as well as multivariate outcomes that are
based on combinations of different endpoints), analysis strategies and commonly
used evaluation criteria.

doParallel, doRNG, foreach, MASS, mvtnorm, stats, survival,
utils

flextable, knitr, officer, rmarkdown, pander

Power and Sample Size Calculations for Clinical Trials
c(
person("Alex", "Dmitrienko", email = "admitrienko@mediana.us", role = c("aut", "cre")))
Rcpp (>= 0.12.10), RcppNumerical, methods, officer, flextable,
devEMF, mvtnorm, shiny, shinydashboard, shinyMatrix, foreach,
parallel, doParallel, doRNG, MASS, rootSolve, lme4, lmerTest,
pbkrtest
Efficient simulation-based power and sample size calculations are supported for a
broad class of late-stage clinical trials. The following modules are included in
the package:
Adaptive designs with data-driven sample size or event count re-estimation,
Adaptive designs with data-driven treatment selection,
Adaptive designs with data-driven population selection,
Optimal selection of a futility stopping rule,
Event prediction in event-driven trials,
Adaptive trials with response-adaptive randomization (experimental module),
Traditional trials with multiple objectives (experimental module).
Traditional trials with cluster-randomized designs (experimental module).