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Predictive markers in a randomized trial with a binary outcome

Stuart Baker
Organization: National Cancer Institute

A predictive marker is a baseline variable in a randomized trial that is used to determine subgroups in which the effect of treatment is greater than average. This software uses a modified adaptive signature design to evaluate a randomized trial with a binary outcome and multiple baseline variables (possibly high dimensional). The software splits the data into training and test samples. A key option is a single split or multiple random splits. For the training sample the software fits various benefit functions. For the test sample the software computes benefit scores based on the benefit function and treatment effect in subgroups with benefit scores greater than cutpoints. The software plots estimated treatment effect versus cutpoint, which is similar to a tail-oriented subpopulation treatment effect pattern plot. The cadit function is not available in this version.

Reference: Baker SG and Bonetti M. Evaluating markers for guiding treatment. Journal of the National Cancer Institute 2016; 108:djw101

*Mathematics > Probability and Statistics
*Wolfram Technology > Mathematica

Subpopulation treatment effect pattern plot, STEPP, treatment selection marker, randomized trial, subgroup analysis

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