R: Simulation-Based Assessment of Covariate Adjustment in Randomized Trials

# Simulation-Based Assessment of Covariate Adjustment in Randomized Trials

## Documentation for package ‘carts’ version 0.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

%join% | Add additional covariates to existing covariate random generator
aggrsurv | Aggregate data in counting process format
append<- | Assignment function to append values to existing list
append<-.list | Assignment function to append values to existing list
bisection | Root finding by bisection
covar_add | Add additional covariates to existing list of covariates
covar_bootstrap | Sample from empirical distribution of covariate data
covar_join | Add additional covariates to existing covariate random generator
covar_loggamma | Simulate from a log gamma-gaussian copula distribution
covar_normal | Simulate from multivariate normal distribution
derive_covar_distribution | Derive covariate distribution from covariate data type
estimate_covar_model_full_cond | Full conditional covariate simulation model
est_adj | Construct estimator for the treatment effect in RCT based on covariate adjustment
est_gee | Construct estimator for the treatment effect in RCT
est_geebin | Construct estimator for the treatment effect in RCT
est_glm | Construct estimator for the treatment effect in RCT
est_glmbin | Construct estimator for the treatment effect in RCT
est_phreg | Marginal Cox proportional hazards model for the treatment effect in RCT
get_factor_levels | Get levels for factor columns in data.table
join_covar | Add additional covariates to existing covariate random generator
optim_sa | Root solver by Stochastic Approximation
outcome_binary | Simulate from binary model given covariates
outcome_continuous | Simulate from continuous outcome model given covariates
outcome_count | Simulate from count model given covariates
outcome_lp | Calculate linear predictor from covariates
outcome_phreg | Outcome model for time-to-event end-points (proportional hazards)
outcome_recurrent | EXPERIMENTAL: Outcome model for recurrent events with terminal events end-points
outcome_shared | Outcome model
rmvn | Multivariate normal distribution function
rnb | Simulate from a negative binomial distribution
sample_covar_parametric_model | Sample from an estimated parametric covariate model
setallargs | Set default arguments of a function
setargs | Set default arguments of a function
Trial | R6 class for power and sample-size calculations for a clinical trial
trial.estimates-class | trial.estimates class object