Estimating Marginal Effects with Prognostic Covariate Adjustment


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Documentation for package ‘postcard’ version 1.1.0

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coef.rctglm Methods for objects of class 'rctglm'
default_learners Creates a list of learners
est Methods for objects of class 'rctglm'
estimand Methods for objects of class 'rctglm'
estimand.rctglm Methods for objects of class 'rctglm'
fit_best_learner Find the best learner in terms of RMSE among specified learners using cross validation
glm_data Generate data simulated from a GLM
options postcard Options
plot.postcard_rpl Create data and plot power curves calculated using functions in 'power_linear()' for a list of formulas/models
plot.postcard_rpm Create data and plot power curves calculated using 'power_marginaleffect()' for a list of models
power_gs Power and sample size estimation for linear models
power_linear Power and sample size estimation for linear models
power_marginaleffect Power approximation for estimating marginal effects in GLMs
power_nc Power and sample size estimation for linear models
predict.rctglm Methods for objects of class 'rctglm'
print.rctglm Methods for objects of class 'rctglm'
prog Extract information about the fitted prognostic model
prog.rctglm_prog Extract information about the fitted prognostic model
rctglm Fit GLM and find any estimand (marginal effect) using plug-in estimation with variance estimation using influence functions
rctglm_methods Methods for objects of class 'rctglm'
rctglm_with_prognosticscore Use prognostic covariate adjustment when fitting an rctglm
repeat_power_linear Create data and plot power curves calculated using functions in 'power_linear()' for a list of formulas/models
repeat_power_marginaleffect Create data and plot power curves calculated using 'power_marginaleffect()' for a list of models
samplesize_gs Power and sample size estimation for linear models
variance_ancova Power and sample size estimation for linear models