How to control confounding effects by statistical analysis

By Mohamad Amin Pourhoseingholi, A. Baghestani, Mohsen Vahedi. Gastroenterology and hepatology from bed to bench, 2012

610 citations recorded by the source corpus.

A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching. But all these methods are applicable at the time of study design. When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects. These Statistical models (especially regression models) are flexible to eliminate the effects of confounders.

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Citations in the corpus

Robust Text Classification in the Presence of Confounding Bias (2016, methodology): “In the social sciences, many methods have been developed to control for confounders, including matching, strat-iﬁcation, and regression analysis (Rosenbaum and Rubin 1983; Pourhoseingholi, Baghestani, and Vahedi 2012).”