Online Causal Inference Seminar

Tuesday, February 21, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)

• Speaker: Ingeborg Waernbaum (Uppsala University)
• Title: Selection bias and multiple inclusion criteria in observational studies
• Discussant: Maya Mathur (Stanford University)
• - Abstract: Selection bias can be a result of applying inclusion/exclusion criteria when selecting the study population in an observational study. The bias can threaten the validity of the study and sensitivity analysis for assessing the effect of the selection is desired. Bounds for selection bias based on values of sensitivity parameters were previously proposed by Smith and VanderWeele (SV). The sensitivity parameters describe aspects of the joint distribution of the outcome, selection and a vector of unmeasured variables, for each treatment group respectively. In this work we extend the sensitivity analysis and give additional guidance for practitioners to construct i) the sensitivity parameters for multiple selection variables, and ii) an alternative assumption free bound, producing only feasible values. In addition to a tutorial for the bounds and an R package, we derive additional properties for the SV bound. We show that the sensitivity parameters are variation independent and derive feasible regions for them. Furthermore, a sharp bound is defined as a bound where it is a priori known that the bias can be equal to the value of the bound, given the values of the selected sensitivity parameters. Conditions for the SV bound to be sharp are provided that can be check with the observed data. We illustrate both the R package and the properties of the bound with a simulated dataset that emulates a study where the effect of zika virus on microcephaly in Brazil is investigated. These are joint work with Stina Zetterström, Department of Statistics, Uppsala University.