Speaker: Stina Zetterström, Department of Statistics, Uppsala University
Abstract: Selection bias is a systematic error that can occur when subjects are included or excluded in the analysis based upon some selection criteria for the study population. This type of bias can threaten the validity of the study and, therefore, methods for estimating the effect of selection bias are desired. One method of estimating the effect of selection bias is through sensitivity analysis, and one such type of sensitivity analysis is bounding the bias. In this work, we investigate a previously proposed bound for average causal effects in the total population and in the selected subpopulation, referred to as the SV bound (Smith and Vanderweele, 2019). The bound is based on assumptions of values of sensitivity parameters selected by the researcher. Furthermore, we derive feasible regions for the sensitivity parameters as well as conditions for the SV bound to be sharp, where sharp means that the bias can take the value of the bound. As an alternative, we propose a second bound that is based solely on the observed data and is, therefore, referred to as the assumption free (AF) bound. We provide an R package for calculating the SV and AF bounds. The bounds and the R package are illustrated with a simulated dataset that emulates a study on the effect of zika virus on microcephaly in Brazil.
This is a joint work with Prof. Ingeborg Waernbaum Department of Statistics, Uppsala University
