A Bayesian Approach to the Instrumental Variable Problem with a Binary Outcome and a Binary Treatment

• Activity Number: 664
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Rodney Sparapani*+ and Purushottam Laud and Jessica Pruszynski and Robert McCulloch
• Companies: Medical College of Wisconsin and Medical College of Wisconsin and Medical College of Wisconsin and The University of Texas
• Keywords: Bayesian inference ; instrumental variables ; logistic regression ; auxiliary variables ; Markov chain Monte Carlo ; comparative effectiveness research

Abstract

In biostatistical investigations, randomized controlled clinical trials are often the gold standard of evidence for determining the safety and efficacy of a treatment. However, clinical trials are often performed on a fairly restricted population; in some cases these trials cannot be performed at all. Non-randomized, observational studies are another type of evidence. In non-randomized studies, the treatment may be confounded with the patient's diagnostic/prognostic information. The Instrumental Variable (IV) approach was created in econometrics to adjust for such confounding, although for a continuous treatment and a continuous outcome. Many biostatistical investigations require a binary treatment and a binary outcome. We will present a Bayesian model that adapts IV to this case. We will compare the performance of this model with current econometric methods via repeated data simulation and standard measures such as bias, mean square error and length/coverage of interval estimates.