Simulation-Based Assessment of Covariate Adjustment in Randomized Trials
Simulate from a negative binomial distribution
Sample from an estimated parametric covariate model
Set default arguments of a function
carts: Simulation-Based Assessment of Covariate Adjustment in Randomiz...
Construct estimator for the treatment effect in RCT
Marginal Cox proportional hazards model for the treatment effect in RC...
Aggregate data in counting process format
Assignment function to append values to existing list
Root finding by bisection
Add additional covariates to existing list of covariates
Sample from empirical distribution of covariate data
Add additional covariates to existing covariate random generator
Simulate from a log gamma-gaussian copula distribution
Simulate from multivariate normal distribution
Derive covariate distribution from covariate data type
Construct estimator for the treatment effect in RCT based on covariate...
Full conditional covariate simulation model
Get levels for factor columns in data.table
Root solver by Stochastic Approximation
Simulate from binary model given covariates
Simulate from continuous outcome model given covariates
Simulate from count model given covariates
Calculate linear predictor from covariates
Outcome model for time-to-event end-points (proportional hazards)
EXPERIMENTAL: Outcome model for recurrent events with terminal events ...
Outcome model
Multivariate normal distribution function
trial.estimates class object
R6 class for power and sample-size calculations for a clinical trial
Monte Carlo simulation framework for different randomized clinical trial designs with a special emphasis on estimators based on covariate adjustment. The package implements regression-based covariate adjustment (Rosenblum & van der Laan (2010) <doi:10.2202/1557-4679.1138>) and a one-step estimator (Van Lancker et al (2024) <doi:10.48550/arXiv.2404.11150>) for trials with continuous, binary and count outcomes. The estimation of the minimum sample-size required to reach a specified statistical power for a given estimator uses bisection to find an initial rough estimate, followed by stochastic approximation (Robbins-Monro (1951) <doi:10.1214/aoms/1177729586>) to improve the estimate, and finally, a grid search to refine the estimate in the neighborhood of the current best solution.
Useful links