HKS Authors

See citation below for complete author information.

Ethel Zimmerman Wiener Professor of Public Policy, HKS; Henry and Allison McCance Professor of Business Administration, HBS


Differences in patient characteristics across trials may bias efficacy estimates from indirect treatment comparisons. To address this issue, matching-adjusted indirect comparison (MAIC) measures treatment efficacy after weighting individual patient data to match patient characteristics across trials. To date, however, there is no consensus on how best to implement MAIC. To address this issue, we applied MAIC to measure how two attention-deficit/hyperactivity disorder (ADHD) treatments (guanfacine extended release and atomoxetine hydrochloride) affect patients' ADHD symptoms, as measured by the ADHD Rating Scale IV score. We tested MAIC sensitivity to: matched patient characteristics, matched statistical moments, weighting matrix, and placebo-arm matching (i.e., matching on outcomes in the placebo arm).


Shafrin, Jason, Anshu Shrestha, Amitabh Chandra, M. Haim Erder, and Vanja Sikirica. "Evaluating Matching-Adjusted Indirect Comparisons in Practice: A Case Study of Patients with Attention Deficit/Hyperactivity Disorder." Health Economics 26.11 (November 2017): 1459-1466.