Comparing Effects of Treatment: Controlling for Confounding.

Abstract

Determining true causal links between an intervention and an outcome forms an imperative task in research studies in neurosurgery. Although the study results sometimes demonstrate clear statistical associations, it is important to ensure that this represents a true causal link. A confounding variable, or confounder, affects the association between a potential predictor and an outcome. To discuss what confounding is and the means by which it can be eliminated or controlled. We identified neurosurgical research studies demonstrating the principles of eliminating confounding by means of study design and data analysis. In this report, we outline the role of confounding in neurosurgical studies after giving an overview of its identification. We report on the definition of confounding and effect modification, and the differences in the 2. We explain study design techniques to eliminate confounding, including simple, block, stratified, and minimization randomization, along with restriction of sample and matching. Data analysis techniques of eliminating confounding include regression analysis, propensity scoring, and subgroup analysis. Understanding confounding is important for conducting a good research study. Study design techniques provide the best way to control for confounders, but when not possible to alter study design, data analysis techniques can also provide an effective control.