Discussing and Displaying the Effectiveness of Causal Models for Observational Studies - THIS LUNCHEON IS SOLD OUT

• Activity Number: 139
• Type: Luncheons
• Date/Time: Monday, August 4, 2003 : 12:30 PM to 1:50 PM
• Sponsor: Section on Health Policy Statistics
• Author(s): Thomas E. Love*+
• Companies: Case Western Reserve University
• Keywords: propensity scores ; instrumental variables ; graphics ; diagnostics ; nonexperimental data ; teaching

Abstract

Approaches designed to draw causal inferences from observational or quasi-experimental data are common parts of studies of health outcomes and policy. Causal interpretations of observed associations in such settings can be tricky, largely due to the problems of selection bias. Methodologies such as propensity scores and instrumental variables are increasingly part of the statistician's toolbox. Statisticians involved with health policy research need to communicate with an audience of physicians and other consumers, who are often skeptical of these techniques. We will discuss the role of statisticians in 1) arguing the need for appropriate methodologies for dealing with self-selection in observational studies; 2) producing effective displays for validating assumptions and documenting findings; and 3) describing an observational study's results, assumptions, caveats and conclusions accurately and usefully for a clinical or policy-oriented audience.