Ch 15 Bias, Confounding, and Interaction

Ch 15 Bias, Confounding, and Interaction Bias “Any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure's effect on the risk of disease."

Selection bias • In reality, exposure and disease are not associated • However, the way in which individuals were selected is such that an apparent association is observed • The apparent association is the result of selection bias.

Study by Ronmark et al. (1999) • A prevalence study of asthma, chronic bronchitis, and respiratory symptoms • Studied the characteristics of nonresponders and the reasons for nonresponse • 9,132 people living in Sweden were invited to participate • Data were obtained by a mailed questionnaire, and the response rate was 85%

Study by Ronmark et al. (1999) • A sample of nonresponders was contacted by telephone • Found a significantly higher proportion of current smokers and manual laborers among the nonresponders than among the responders • The prevalence rates of wheezing, chronic cough, sputum production, attacks of breathlessness, and asthma and use of asthma medications were significantly higher among the nonresponders

Selection bias • Nonresponse may introduce a serious bias that may be difficult to assess • It is important to keep nonresponse to a minimum.

Information bias • Can occur when the means for obtaining information about the subjects are inadequate • As a result some of the information gathered regarding exposures and/or disease outcome is incorrect

Information bias Misclassification bias • Example: In a case-control study, some people who have the disease (cases) may be misclassified as controls, and some without the disease (controls) may be misclassified as cases • This may result, for example, from limited sensitivity and specificity of the diagnostic tests involved or from inadequacy of information derived from medical or other records

Information bias Misclassification bias • If exposure data are based on interviews, for example, subjects may either not be aware of their exposure or may erroneously think that it did not occur • If ascertainment of exposure is based on old records, data may be incomplete

Confounding • In a study of whether factor A is a cause of disease B, we say that a third factor, factor X is a confounder if the following are true: • Factor X is a known risk factor for disease B • Factor X is associated with factor A, but is not a result of factor A

Confounding Example (in Chap 10) • The relationship between coffee and cancer of the pancreas • Found an apparent dose-response relationship between coffee and cancer of the pancreas, particularly in women • It was rare to find a smoker who does not drink coffee

Confounding Example (in Chap 10) • Smoking was a confounder, because although we were interested in a possible relationship between coffee consumption (factor A) and pancreatic cancer (disease B), the following are true of smoking (factor X): • Smoking is a known risk factor for pancreatic cancer • Smoking is associated with coffee drinking, but is not a result of coffee drinking

Confounding • So if an association is observed between coffee drinking and cancer of the pancreas, it may be (1) that coffee actually causes cancer of the pancreas, or (2) that the observed association of coffee drinking and cancer of the pancreas may be a result of confounding by cigarette smoking

Confounding • We observe the association of coffee drinking and pancreatic cancer because cigarette smoking is a risk factor for pancreatic cancer and cigarette smoking is associated with coffee drinking (Fig 15-1)

Confounding • When we observe an association • we ask whether it is causal (Fig. 15-1A) or • whether it is a result of confounding by a third factor that is both a risk factor for the disease and is associated with the exposure in question (see Fig. 15-1B).

Ch 15 Bias, Confounding, and Interaction