Alums Zack Mccaw & Lu Tian and Prof LJ Wei Publish Paper on Analytic Methods for COVID-19 Clinical Trials

graph mean postrecovery times

Zachary McCaw, Lu Tian, Lee-Jen Wei Department alumni Dr. Zachary McCaw, PhD ’19 and Dr. Lu Tian, PhD ’02, and Professor of Biostatistics Dr. Lee-Jen Wei are among the authors who recently published a methods paper on “How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19” in The Annals of Internal Medicine, one of the top 5 medical journals in the world.

The paper discusses concerns about the appropriateness of analytic methods used in recent high-profile clinical trials evaluating treatments of COVID-19, and presents an alternative approach to assessing the treatment effects from new therapies and dealing with the unique features of these trials.


Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan–Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.

Several recent randomized, comparative trials of treatments for coronavirus disease 2019 (COVID-19) used time to a positive outcome, such as improvement or recovery, as either the primary end point or a key secondary end point (1–7). The Supplement Table (in Part A of the Supplement) provides detailed descriptions of the end points, efficacy measures, and analysis results from several recently published studies of COVID-19. This article discusses the issues and challenges that commonly arise in the analysis of such trials, and using examples from 2 trials, presents well-established yet underused analytic procedures that provide robust and clinically interpretable summaries of treatment efficacy.

Dr. McCaw and Dr. Wei also recently published a correspondence in the New England Journal of Medicine on “Remdesivir for the Treatment of Covid-19 — Preliminary Report“.