Working Paper: NBER ID: w27895
Authors: John Mullahy
Abstract: 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.
Keywords: Median Treatment Effects; COVID-19; Clinical Trials; Treatment Effectiveness
JEL Codes: C18; I10; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| remdesivir treatment (C22) | faster recovery (Y60) |
| median outcomes (C41) | insights into treatment effectiveness (C90) |
| decisions based solely on median outcomes (D79) | misleading conclusions (G41) |
| understanding the distribution of treatment effects (C22) | informed decision-making (D87) |