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Gastroenterol Hepatol Bed Bench. 2012 Spring;5(2):79-83.

How to control confounding effects by statistical analysis.

Author information

1
Department of Biostatistics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2
Department of Mathematic, Islamic Azad University - South Tehran Branch, Iran.
3
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

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.

KEYWORDS:

Adjustment; Confounders; Statistical models

PMID:
24834204
PMCID:
PMC4017459

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