To address some of the confounding problems associated with what Harvard T.H. Chan School of Public Health’s David Cheng calls a “naive comparison” of a drug’s effectiveness, some analysts and researchers have turned to matching-adjusted indirect comparison (MAIC), according to a Healthcare Finance report.
“They’d look, say, at the rates of survival for a cancer drug by a given time in one study and then compare them to another, even though the two studies would not be directly comparable,” Cheng tells Healthcare Finance about the “naive” comparisons. “The patients might have more late-stage disease in one study and more early-stage disease in the other, or some other significant difference in patient characteristics, and this wouldn’t be taken into account in the analysis. You’d end up with massive confounding.”
MAIC, Cheng says, could help researchers tackle some of the issues that arise when dealing with individual patient-level data and data summaries from publications on existing drugs.
“If you have access to the individual-level data from one drug trial, then you could reweight the observations or adjust the final analysis so that the patient characteristics match the summaries of another trial,” Cheng said.
To read the full report on Healthcare Finance, click here.