Seminar

Is the healthcare provider an instrumental variable or a confounder?

Wednesday, 5 September 2012
Time: 
09:30 to 10:30
Murdoch Childrens Research Institute, Royal Childrens Hospital (MCRI - RCH), Level 1, Vernon Collins Theatre
Flemington Road
Parkville 3050
Australia

Large non-experimental studies are increasingly used to evaluate the benefits and harms of medical interventions. One of the principal challenges in such studies is confounding - systematic differences between patients exposed to an intervention of interest versus the chosen comparator.

Recently, instrumental variable approaches have been proposed that use variation in treatment preference between providers to estimate treatment effects. Under certain assumptions, these methods allow unbiased estimation of treatment effects even if important confounders are unmeasured.

If the healthcare provider fails to satisfy assumptions of an instrumental variable, it should instead be treated as a potential confounding factor. I will illustrate how the results of non-experimental studies can depend strongly on how one handles the healthcare provider in the analysis.

Although the property of being a confounder or an instrumental variable is not statistically testable, I will provide some ideas about how one can determine the appropriate use of the healthcare provider in non-­experimental studies.

 

Assoc. Prof. Alan Brookhart

Department of Epidemiology
UNC Gillings School of Global Public Health, UNC-Chapel Hill

Dr. M. Alan Brookhart is an Associate Prof of Epidemiology and Medicine at the University of North Carolina at Chapel Hill. His research is focused primarily on the development and application of new statistical methods and study designs for epidemiologic studies of medications using large clinical and healthcare utilization databases. In this area, he has made contributions to the development of quasi-experimental and instrumental variable approaches that can be used to estimate causal effects in the presence of unmeasured or poorly recorded confounding variables.

He has also been involved with the development of propensity score and marginal structural model methodology and has also developed new epidemiologic approaches for studying medication adherence and use of healthcare services. Substantively, he is interested in the effects medications in the elderly and patients with end-stage renal disease.