Adjusting for selection bias in retrospective, case-control studies

Abstract

Retrospective case-control studies are more susceptible to selection bias than other epidemiologic studies as by design they require that both cases and controls are representative of the same population. However, as cases and control recruitment processes are often different, it is not always obvious that the necessary exchangeability conditions hold. Selection bias typically arises when the selection criteria are associated with the risk factor under investigation. We develop a method which produces bias-adjusted estimates for the odds ratio. Our method hinges on 2 conditions. The first is that a variable that separates the risk factor from the selection criteria can be identified. This is termed the "bias breaking" variable. The second condition is that data can be found such that a bias-corrected estimate of the distribution of the bias breaking variable can be obtained. We show by means of a set of examples that such bias breaking variables are not uncommon in epidemiologic settings. We demonstrate using simulations that the estimates of the odds ratios produced by our method are consistently closer to the true odds ratio than standard odds ratio estimates using logistic regression. Further, by applying it to a case-control study, we show that our method can help to determine whether selection bias is present and thus confirm the validity of study conclusions when no evidence of selection bias can be found.

RADHIKa: Ratio-based analysis deriving basis for comparison of historical, parallel or interdependent reported ken of studies - a novel method for comparing interconnected and disconnected data sets

“…Data for logic was taken from an independent study. 9 Our analysis indicates that Optetrack device performs similar to the Logic device (R(E)/ψ = 1.03), when precursors are similar (ψ = 1.01). This observation is in agreement with earlier reported analysis of the pairmatched study.…”

“…9 Therefore, establishing a method for standardization of study outcomes to enhance their scientific acceptance is a priority. Comparing studies either through meta-analyses or numerically are commonly accepted methods.…”

Modifying Effect of N‐Acetyltransferase 2 Genotype on the Association Between Systemic Lupus Erythematosus and Consumption of Alcohol and Caffeine‐Rich Beverages

“…Selection bias may occur if the decision to participate is affected by exposure status. In many cases, selection bias is not extreme enough to have an impact on inference and conclusions (66). As the possibility of recall and selection biases could not be completely excluded in case-control studies, our findings should be interpreted with caution.…”

Linear Models: A Useful “Microscope” for Causal Analysis

“…The two examples above are special cases of a more general phenomenon called "selection bias" which occurs when samples are preferentially selected to the data set, depending on the values of some variables in the model [12][13][14][15]. In Figure 6, for example, if Z ¼ 1 represents the inclusion in the data set, and Z ¼ 0 exclusion, the selection decision is shown to be a function of both X and Y.…”