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Confounding variable Pravin Pisudde
Definition • Also called as • confounding factor • lurking variable • a confound • Confounder • is an extraneous variable in a statistical model that correlates(positively or negatively) with both the dependent variable and the independent variable. • Defined as one, which is associated both with exposure and disease and is distributed unequally in study and control group
Example:1 • Role of alcohol in aetiology of oesophageal cancer • Smoking is confounding factor because • It is asssociated with consumption of alcohol • It is independent risk factor for oesophageal cancer • In this effect of alcohol consumption can be determined only if the influence of smoking is neutralized by matching
Example:2 • Relationship between steriod contraceptive and breast cancer • Age is confounding factor • If women at younger age if takes steriod tablets are at lower risk than those who starts taking tablets at higher age • Breast cancer also common with increasing age • It can be neutralized by matching
Example:3 • Study of coffee drinking and lung cancer. • Smoking is confounding factor • If coffee drinkers were also more likely to be cigarette smokers, and • the study 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. • 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 factor does not confound the study results.
Magnitude of confounding factor depends on • The strength of association of the factor with • the disease under study among the individuals who have not experienced the principle exposure under investigation • the principle exposure under investigation among persons who have not experienced disease under study • Prevalence of the factor • Factor with a very (<0.05) or very high (> 0.95) prevalence rarely generate substantial confounding
facts • The presence of confounding factor and the contemplated stratified analysis impose power restrictions. • The thumb rule that every recognizable major confounder the study size should be increased by about 10 percent to preserve statistical power at the level that would have been achieved in the absence of the confounder
References • MacMohan B, Trichopoulos D. Epidemiology principle and methods. 2nd Ed. New York. Little, Brown and Company. 1992 • K Park. Park’s textbook of preventive and social medicine. 19th Ed. Jabalpur. BanarsidasBhanot Publishers. 2007