Biostatistics Journal Club: Statistical Methods in COVID-Related Studies

When data from randomized trials are not available, causal analyses of observational data may be used to guide practice by adopting a target trial emulation approach. By carefully specifying a target trial’s eligibility criteria, treatment strategies, treatment assignment, outcome, and statistical analysis, the emulated target trial can address the research question and avoid common methodological pitfalls of causal inference from observational data. Wei Wang, PhD, Brigham and Women’s Hospital,  will review the framework for target trial emulation, and discuss a few applications to COVID-19 observational data which can be used to study and compare effectiveness of treatments and vaccines.

Required Reading:

Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available

Association Between Early Treatment With Tocilizumab and Mortality Among Critically Ill Patients With COVID-19

Comparison of Moderna Versus Pfizer-BioNTech COVID-19 Vaccine Outcomes: A Target Trial Emulation Study in the U.S. Veterans Affairs Healthcare System

Speaker Bio:

Wei Wang, PhD, is an associate biostatistician in the Departments of Medicine and Neurology at Brigham and Women’s Hospital (BWH), and an assistant professor in the Division of Sleep Medicine at Harvard Medical School. Her research has focused on clinical trials, experimental design, longitudinal and function data analyses, and survival analysis. She serves as a BWH biostatistics consultant for the Harvard Catalyst Biostatistics program, and has collaborated with many investigators in the Harvard/BWH community. She has participated in several studies to investigate treatments and vaccines for COVID-19.