Expansion of Methods for Two-Stage Trial Designs for Testing Treatment, Self-Selection, and Treatment Preference Effects [Methods Study], 2016-2020

A patient's preference for a treatment may affect how well the treatment works. For example, if patients prefer a specific medicine, they may be more likely to take that medicine.
Traditional randomized clinical trials can't tell how much patient preferences affect how well a treatment works. But a two-stage clinical trial might. In a two-stage trial, researchers assign patients by chance to one of two groups. In the first group, researchers assign patients by chance to get a specific treatment, regardless of their preference. In the second group, patients choose their treatment. In a two-stage trial, researchers can compare health outcomes for patients who choose their treatment with patients who don't. But few methods exist for researchers to design and analyze this type of trial.
In this project, the research team developed new statistical methods for two-stage trials. The team wanted to find out how many patients are needed for two-stage trials to provide accurate results. They also wanted to learn how to measure whether patient preference for a specific treatment affects patients' health outcomes.
To access the software, methods and R package, please visit the preference CRAN webpage and preference GitHub.