Two basic statistical strategies of conducting causal inference in real-world studies

Randomized controlled clinical trials are the gold standard in drug development, but their costs, duration, and limited generalizability have motivated some to look for real-world studies as alternatives. On the other hand, real-world studies may be less convincing due to the presence of confounding bias. In the literature of causal inference, a variety of statistical methods have been proposed to adjust for confounding bias. However, it is challenging for the users to understand the statistical properties enjoyed by each method and then explicitly specify its underlying model assumptions.

Source: Contemporary Clinical Trials - October 23, 2020 Category: Radiology Authors: Yixin Fang Source Type: research