Statistical Analysis of SMART Studies via Artifcial Randomization
Thursday, November 8, 2018 - 5:00pm - 5:30pm
Hypothesis testing to compare dynamic treatment regimes (DTR) from a sequential multiple assignment randomization trial (SMART) is generally based on inverse probability weighting or g-estimation. However, regression methods allowing for comparison of DTRs that flexibly adjust for baseline covariates using these methods are not as straight-forward due to the fact that one patient can belong to multiple DTRs. This poses a challenge for data analysts as it violates basic assumptions of regression modeling of unique group membership. In this talk, we will propose an “artificial randomization technique to make the data appear that each subject belongs to a single DTR. This enables treatment strategy indicators to be inserted as covariates in a regression model and apply standard tests such as t- or F-tests. The properties of this method are investigated analytically and through simulation. We demonstrate the application of this method by applying to a SMART study of cancer regimes.