Improving upon Intention-to-treat Analysis When Clinical Trials Become Open-label

• Activity Number: 212
• Type: Topic Contributed
• Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Samantha R. Cook*+ and Donald B. Rubin
• Companies: Harvard University and Harvard University
• Keywords: historical controls ; missing data ; multiple imputation ; hierarchical models

Abstract

In a recent FDA trial, the drug under investigation became 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 missing. We propose a method to impute placebo controls' missing outcomes, as if they had stayed on placebo. This 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. Using multiple imputation will allow for improved estimation of the treatment effect and a more powerful test of the drug's effectiveness.