B1396
Title: Selection bias and multiple inclusion criteria
Authors: Ingeborg Waernbaum - Uppsala University (Sweden) [presenting]
Abstract: Spurious associations between an exposure and outcome not describing the causal estimand of interest can be the result of the selection of the study population. Recently, sensitivity parameters and bounds have been proposed for selection bias, along the lines of sensitivity analysis previously proposed for bias due to unmeasured confounding. The basis for the bounds is that the researcher specifies values for sensitivity parameters describing associations under additional identifying assumptions. We extend the previously proposed bounds to give additional guidance for practitioners to construct i) the sensitivity parameters for multiple selection variables and ii) an alternative assumption-free bound, producing only logically feasible values. The results show that the assumption-free bounds can be both smaller and larger than the previously proposed bounds and, therefore, can serve as an indicator of settings when the former bounds do not produce feasible values. We derive the bounds in a study of perinatal risk factors for childhood-onset type 1 diabetes mellitus where the selection of the study population was made by multiple inclusion criteria. It may be difficult for the researcher to give plausible input values for the sensitivity parameters for selection bias under multiple selection and to provide further guidance for practitioners, we provide a data learner in R where both the sensitivity parameters and the assumption free bounds are implemented.
