pact: Predictive Analysis of Clinical Trials

Based on response and covariate data from a randomized clinical trial comparing a new experimental treatment E versus a control C, the purpose behind the functions in pact is to develop and internally validate a classifier that can identify subjects likely to benefit from E rather than C. Currently, survival and binary response types are permitted. Covariate data can be high-dimensional as well, and currently, the dimension reduction techniques lasso and univariate variable selection are implemented. These dimesion reduction options can be used with low-dimensional covariates too if the user so desires. The user can optionally specify a second (small) set of prognostic variables to always remain in the model. This set of variables will not be subjected to variable selection.