A Cost/Benefit Analysis of Clinical Trial Designs for Covid-19 Vaccine Candidates

54 Pages Posted: 7 Oct 2020 Last revised: 8 Mar 2021

See all articles by Donald Berry

Donald Berry

affiliation not provided to SSRN

Scott Berry

Berry Consultants, LLC

Peter Hale

affiliation not provided to SSRN

Leah Isakov

Pfizer, Inc.

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering; Santa Fe Institute

Kien Wei Siah

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering

Chi Heem Wong

Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: October 2020

Abstract

We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional randomized clinical trials and adaptive and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 504 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits—averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design—if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.

Suggested Citation

Berry, Donald and Berry, Scott and Hale, Peter and Isakov, Leah and Lo, Andrew W. and Siah, Kien Wei and Wong, Chi Heem, A Cost/Benefit Analysis of Clinical Trial Designs for Covid-19 Vaccine Candidates (October 2020). NBER Working Paper No. w27882, Available at SSRN: https://ssrn.com/abstract=3705092

Donald Berry (Contact Author)

affiliation not provided to SSRN ( email )

Scott Berry

Berry Consultants, LLC

Austin, TX
United States

Peter Hale

affiliation not provided to SSRN

Leah Isakov

Pfizer, Inc. ( email )

MA 01776
United States

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering ( email )

100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)

HOME PAGE: http://web.mit.edu/alo/www

Santa Fe Institute

1399 Hyde Park Road
Santa Fe, NM 87501
United States

Kien Wei Siah

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering ( email )

Cambridge, MA
United States

Chi Heem Wong

Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL) ( email )

Stata Center
Cambridge, MA 02142
United States

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
Cambridge, MA 02142
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
20
Abstract Views
119
PlumX Metrics