![]() ![]() The end result is that the true relationship between the IV and DV is somewhat disguised because of the possibility that another variable (the confounding variable) has influenced the outcome of the study in an unanticipated way. In such cases, any differences between the two groups on a DV might very well be the result of the uncontrolled extraneous variable (i.e., confounding variable), because that variable has the effect of confusing, or confounding, proper interpretation of the study. As a result, the variable becomes a confounding variable. If an extraneous variable is not appropriately controlled, it may be unequally present in the comparison groups. ![]() Hymel, in Clinical Massage in the Healthcare Setting, 2008 Confounding Variable. As a rule of thumb, logistic regression must have at least 10 outcome events for every variable adjusted in the model, whereas linear regression requires 10–15 outcomes per variable included in the model to prevent overfitting. Third, regression model estimates are not very reliable when there are very few outcome events. Second, every potential confounding variable added to a statistical model decreases the model’s statistical power and thereby increases the chance of resulting in a false-negative result (i.e., type II error). First, only recognized confounders can be addressed in the regression model. ![]() However, statistical risk adjustment has several important limitations. Both of these approaches involve taking into account differences in the prevalence of recognized confounders across comparison groups. Logistic regression models are used when the outcome variable is binary, whereas linear regression is used when the outcome is continuous. The most common technique is multivariate regression analysis, including linear and logistic regression models. In addition to minimizing confounding through good study design, confounding can also be addressed during the analytic phase of a study with statistical risk-adjustment techniques. In comparison, matching refers to using a comparison group of unexposed (control) subjects who are identical to the exposed (case) subjects across a set of characteristics (e.g., age, sex, residence) that have the potential to result in confounding. However, restrictive entry criteria can sometimes limit generalizability. Restriction refers to the tight control of study entry criteria, for example, only enrolling patients undergoing elective surgery and excluding emergent procedures. When randomization is not practical, restriction or matching can be used to prevent confounding. Thus, whereas the baseline rate of outcomes in the entire cohort might be influenced by these factors, the differences across comparison groups are less likely to be affected. When subjects are randomized, potentially confounding variables (both recognized and unrecognized) are likely to be evenly distributed across comparison groups. In the design of a study, confounding is most effectively addressed with randomization. Confounding can be minimized in several ways, in both the design of the study and the analysis of the study’s results. In evaluating the strength of evidence in a published study, readers must assess how well the researchers accounted for the potential effect of confounding. In this example, the severity of the illness is a confounder in the observed association between mortality and surgical approach. For example, a comparison of mortality after open versus laparoscopic colectomy might be skewed because of the greater likelihood of open colectomy being performed as an emergency procedure in critically ill patients with perforation. ![]() Confounding variables influences both the outcome variable and exposure variable causing a spurious association. These differences may occur due to selection bias that distributes risk factors known as confounding variables unevenly between comparison groups. Townsend JR., MD, in Sabiston Textbook of Surgery, 2022 ConfoundingĬonfounding refers to differences in outcomes that occur because of differences in the baseline risks of the comparison groups. ![]()
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