One of my papers on matching-adjusted indirect comparisons (MAIC) was published today, with co-authors Anshu Shrestha, Amitabh Chandra, M. Haim Erder, and Vanja Sikirica. The title is: “Evaluating Matching-Adjusted Indirect Comparisons in Practice: A Case Study of Patients with Attention-Deficit/Hyperactivity Disorder” and the abstract is below. Check it out!
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). After applying MAIC, guanfacine and atomoxetine had similar reductions in ADHD symptoms (Δ: 0.4, p < 0.737). The results were similar for three of four sensitivity analyses. When we applied MAIC with placebo-arm matching, however, guanfacine reduced symptoms more than atomoxetine (Δ: −3.9, p < 0.004). We discuss the implication of this finding and advise MAIC practitioners to carefully consider the use of placebo-arm matching, depending on the presence of residual confounding across trials.
Shafrin, J., Shrestha, A., Chandra, A., Erder, M. H., and Sikirica, V. (2016) Evaluating Matching-Adjusted Indirect Comparisons in Practice: A Case Study of Patients with Attention-Deficit/Hyperactivity Disorder.