![]() In many studies, confounders are not adjusted because they were not measured during the process of data gathering. Confounding is better taken care of by randomization at the design stage of the research ( 6).Ī successful randomization minimizes confounding by unmeasured as well as measured factors, whereas statistical control that addresses confounding by measurement and can introduce confounding through inappropriate control ( 7– 9).Ĭonfounding can persist, even after adjustment. The inclusion of this analysis can increase the statistical power.Ĭonfounders are common causes of both treatment/exposure and of response/outcome. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. ANCOVA is a combination of ANOVA and linear regression. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects ( 4). Matching is commonly used in case-control studies (for example, if age and sex are the matching variables, then a 45 year old male case is matched to a male control with same age).īut all these methods mentioned above are applicable at the time of study design and before the process of data gathering. Matching which involves selection of a comparison group with respect to the distribution of one or more potential confounders. Restriction eliminates variation in the confounder (for example if an investigator only selects subjects of the same age or same sex then, the study will eliminate confounding by sex or age group). This reduces potential for confounding by generating groups that are fairly comparable with respect to known and unknown confounding variables. In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders. There are various ways to modify a study design to actively exclude or control confounding variables ( 3) including Randomization, Restriction and Matching. Thus, confounding is a major threat to the validity of inferences made about cause and effect (internal validity). ![]() In this case the researchers are said to account for their effects to avoid a false positive (Type I) error (a false conclusion that the dependent variables are in a casual relationship with the independent variable). The researchers therefore need to account for these variables - either through experimental design and before the data gathering, or through statistical analysis after the data gathering process. Simpson's paradox refers to the reversal of the direction of an association when data from several groups are combined to form a single group. Simpson's paradox too is another classic example of confounding ( 2). However, if a confounding factor (in this example, smoking) is recognized, adjustments can be made in the study design or data analysis so that the effects of confounder would be removed from the final results. If the person who entered in the study as a coffee drinker was also more likely to be cigarette smoker, and the study only measured coffee drinking but not smoking, the results may seem to show that coffee drinking increases the risk of lung cancer, which may not be true. A hypothetical example would be a study of relation between coffee drinking and lung cancer. There may be also other factors that are associated with the exposure and affect the risk of developing the disease and they will distort the observed association between the disease and exposure under study. The aim of major epidemiological studies is to search for the causes of diseases, based on associations with various risk factors. A Confounder is an extraneous variable whose presence affects the variables being studied so that the results do not reflect the actual relationship between the variables under study. Confounding variables or confounders are often defined as the variables correlate (positively or negatively) with both the dependent variable and the independent variable ( 1).
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