Events

Marc Scott, (NYU)

Sensitivity Analysis for Grouped Data: Bounding the Bias in a Multilevel Setting

Abstract:We are concerned with the unbiased estimation of a treatment effect in the context of observational studies with grouped or multilevel data. When analysing such data with this goal, practitioners typically include as many predictors (controls) as possible, in an attempt to satisfy ignorability. In one approach, the econometric “within-group" treatment estimator, one includes indicator variables to control for unobserved between-group variation. However, depending on the mathematical properties of the underlying stochastic process, adding such predictors can actually increase bias in the targeted treatment effect.

Exploiting information contained in successive multilevel model specifications and estimates, we generate bounds on the potential differences in bias for several competing estimators, informing model selection. Our approach relies on a parametric model for grouped data and omitted confounders and establishes a framework for sensitivity analysis in the multilevel modelling context. We characterize the strength of the confounding and corresponding bias amplification using easily interpretable parameters and graphical displays.

We apply this approach to data from a multinational educational evaluation study. We demonstrate the extent to which different treatment effect estimators are robust to potential unobserved individual- and group-level confounding. Time permitting, we re-frame the sensitivity analysis for these data using a Bayesian approach. The latter reveals simultaneously the uncertainty we have about unobserved confounders and the implied treatment effects.

Joint work with Jennifer Hill (NYU), Joel Middleton (UC/Berkeley) & Ronli Diakow (NYC DOE & NYU).

Simon Spencer, (University of Warwick)

Bayesian methods for source attribution and spatio-temporal modelling of campylobacteriosis.

Abstract: Campylobacteriosis is a common form of food poisoning with a complex epidemiology. Despite the large number of cases, the dominant pathways to infection were until recently poorly understood. In this talk I will outline how novel statistical methodology has helped to develop our understanding of this pathogen as part of a multidisciplinary approach spanning epidemiology, genetics, public health and evolutionary biology.

In particular I will outline how to estimate the probability that a human infection has emanated from a potential host species; how to identify risk factors from the spatial distribution of cases; and how to detect outbreaks from a background of sporadic cases.

Joint work with Petra Müllner and Nigel French.

14.00 - 15.00: Marc Scott, Sensitivity Analysis for Grouped Data: Bounding the Bias in a Multilevel Setting
15.00 - 15.30: Coffee break
15.30 - 16.30: Simon Spencer, Bayesian methods for source attribution and spatio-temporal modelling of campylobacteriosis