Discovering Treatment Effectiveness via Median Treatment Effects—Applications to COVID-19 Clinical Trials
Comparing median outcomes to gauge treatment effectiveness is widespread practice in clinical and other investigations. While common, such difference-in-median characterizations of effectiveness are but one way to summarize how outcome distributions compare. This paper explores properties of median treatment effects as indicators of treatment effectiveness. The paper's main focus is on decisionmaking based on median treatment effects and it proceeds by considering two paths a decisionmaker might follow. Along one, decisions are based on point-identified differences in medians alongside partially identified median differences; along the other decisions are based on point-identified differences in medians in conjunction with other point-identified parameters. On both paths familiar difference-in-median measures play some role yet in both the traditional standards are augmented with information that will often be relevant in assessing treatments' effectiveness. Implementing both approaches is shown to be straightforward. In addition to its analytical results the paper considers several policy contexts in which such considerations arise. While the paper is framed by recently reported findings on treatments for COVID-19 and uses several such studies to explore empirically some properties of median-treatment-effect measures of effectiveness, its results should be broadly applicable.
Thanks are owed to Chris Adams, Marguerite Burns, Domenico Depalo, Chuck Manski, Dan Millimet, Ciaran O'Neill, and Jon Skinner for helpful comments on an earlier draft. NICHD grant P2CHD047873 to the Center for Demography and Ecology and NIA grant P30AG017266 to the Center for Demography of Health and Aging, both at UW-Madison, provided logistical support. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.
John Mullahy, 2021. "Discovering treatment effectiveness via median treatment effects—Applications to COVID‐19 clinical trials," Health Economics, vol 30(5), pages 1050-1069.