Fit and Tune Models to Detect Treatment Effect Heterogeneity
Get the MNPP for a Classification Tree
Get the MNPP for a Conditional Inference Tree
Get the MNPP for a Model fit via Lasso
Get the MNPP for the Step 2 model
Get the MNPP for a Regression Tree
Permute a dataset under the null hypothesis and get the MNPP
Get the appropriate Step 1 estimation function associated with a metho...
Get the appropriate Step 2 estimation function associated with a metho...
Generate a dataset with permuted treatment indicators
Print an object of class tunevt
Test if a Value Gives a Null Conditional Inference Tree
Estimate the penalty parameter for Step 2 of Virtual Twins
Fit a tuned Virtual Twins model
Check if alpha0 is a valid input to tunevt
Check if p_reps is a valid input to tunevt
Check if Trt is a valid input to tunevt
Check if Y is a valid input to tunevt
Estimate the CATE Using the Lasso for Step 1 of Virtual Twins
Estimate the CATE Using MARS for Step 1 of Virtual Twins
Estimate the CATE Using a Random Forest for Step 1 of Virtual Twins
Estimate the CATE Using Super Learner for Step 1 of Virtual Twins
Estimate the CATE using a classification tree for Step 2
Estimate the CATE using a conditional inference tree for Step 2
Estimate the CATE using the Lasso for Step 2
Estimate the CATE using a regression tree for Step 2
Implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.