In a recent FDA trial, the drug under investigation was made commercially available before the end of the trial. Patients in the trial therefore had the option of going off trial protocol and obtaining the commercially available active therapy. When patients randomized to placebo switch to commercially available therapy, they cease to be controls in the usual sense: All measurements after a placebo control switches to active therapy are treated as missing. We propose a method to impute placebo controls' missing outcomes, as if they had stayed on placebo. There are two key phases to this process.
The first phase selects the historical patients who look as if they could have plausibly been enrolled in a similar randomized trial at some point in their observation history. There are two particular complications to this selection process. The first is missing covariate data, which is dealt with by estimating propensity scores using a general location model. The second complication is the need to define baseline for each of the historical patients.
The second phase, the imputation phase, involves first, fitting a Bayesian hierarchical regression model to data from untreated historical patients; second, incorporating information learned from the historical patients into a similar model for placebo controls; and third, using this model and observed on-protocol data to impute missing values for placebo controls who switched to active therapy. Once missing values have been multiply imputed, the completed data sets can be analyzed as planned, and their results combined in a straightforward way.
This session will consist of three talks. Jim MacDougall of Genzyme will first present an overview of the trial. The second talk, by Elizabeth Stuart, describes the process of selecting the historical patients. In the third talk, Samantha Cook will describe the imputation model used to impute the missing placebo outcomes.