Reporting and Selection Bias in Case-Control Studies of Congenital Malformations
Shanna H. Swan, Gary M. Shaw, Jane Schulman
Abstract
Shanna H. Swan, Gary M. Shaw, Jane Schulman
Abstract
Retrospective studies of congenital malformations frequently rely on exposures reported by study subjects. Differential error in exposure reporting by cases and controls, which has alternatively been referred to as "recall bias" and "reporting bias," may result in a biased effect measure. Some authors have attempted to avoid reporting bias by comparing exposures between two malformed groups, rather than between cases and nonmalformed controls. This approach, however, may introduce its own bias, which we call selection bias. Both reporting bias and selection bias are shown to be algebraically equivalent to bias arising from exposure misclassification. The magnitudes of these biases are compared for a range of plausible parametric values. The case-control design is sensitive to both differential reporting and selection bias, and the choice of study design involves balancing these two sources of bias.
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Retrospective studies of congenital malformations frequently rely on exposures reported by study subjects. Differential error in exposure reporting by cases and controls, which has alternatively been referred to as "recall bias" and "reporting bias," may result in a biased effect measure. Some authors have attempted to avoid reporting bias by comparing exposures between two malformed groups, rather than between cases and nonmalformed controls. This approach, however, may introduce its own bias, which we call selection bias. Both reporting bias and selection bias are shown to be algebraically equivalent to bias arising from exposure misclassification. The magnitudes of these biases are compared for a range of plausible parametric values. The case-control design is sensitive to both differential reporting and selection bias, and the choice of study design involves balancing these two sources of bias.
Key concepts: Selection bias, Recall bias, Information bias, Reporting bias, Selection (genetic algorithm), Statistics, Epidemiology, Medicine