Adds support for classification trees in Step 2 by setting
step2 = 'classtree' with a given threshold of
threshold.
Adds the print.tunevt method.
Fixes a bug where zbar was calculated using the mean
difference in the first column of the data instead of using the location
of the variable Y.
Adds the parallel option to tunevt to
support parallel backends.
This patch reconciles an invalid URI in the tunevt
documentation’s references.
This is a new package that implements the Virtual Twins algorithm for subgroup identification (Foster et al., 2011) while controlling the probability of falsely detecting differential treatment effects when the conditional treatment effect is constant across the population of interest. These methods were originally presented in Wolf et al. (2022).
Foster, J. C., Taylor, J. M., & Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine, 30(24), 2867–2880. https://doi.org/10.1002/sim.4322
Wolf, J. M., Koopmeiners, J. S., & Vock, D. M. (2022). A permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type-I error rate. Clinical Trials. https://doi.org/10.1177/17407745221095855
tunevt() fits a Virtual Twins model using
user-specified Step 1 and Step 2 models with parameter selection to
control the probability of a false discovery.