Names of the institutions where research was conducted: Department of Research and Evaluation, Southern California Permanente Medical Group & University of Southern California.
INCREMENTAL EXPENDITURE OF BIOLOGIC DISEASE MODIFYING ANTIRHEUMATIC TREATMENT USING INSTRUMENTAL VARIABLES IN PANEL DATA
Version of Record online: 20 JUN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Volume 22, Issue 7, pages 807–823, July 2013
How to Cite
Kawatkar, A. A., Hay, J. W., Stohl, W. and Nichol, M. B. (2013), INCREMENTAL EXPENDITURE OF BIOLOGIC DISEASE MODIFYING ANTIRHEUMATIC TREATMENT USING INSTRUMENTAL VARIABLES IN PANEL DATA. Health Econ., 22: 807–823. doi: 10.1002/hec.2855
- Issue online: 7 JUN 2013
- Version of Record online: 20 JUN 2012
- Manuscript Accepted: 28 MAY 2012
- Manuscript Revised: 14 MAY 2012
- Manuscript Received: 3 MAY 2012
- multiple treatment effects;
- instrumental variables;
- generalized method of moments;
- comparative effectiveness;
- rheumatoid arthritis;
- panel data
In health care, decision makers are generally interested in simultaneous comparisons among multiple treatments or interventions available as treatment choices in real-world clinical setting. The lack of random assignment to treatment in real-world clinical settings leads to selection-bias issues when evaluating the marginal benefits of treatment. The application of instrumental variables (IV) estimation to mitigate selection bias has traditionally been limited to comparing only two treatments/interventions concurrently. Using the case of biologic treatment in rheumatoid arthritis, we describe a generalized method of moments (GMM)–based panel data IV (IV-GMM) framework, to simultaneously estimate multiple treatment effects in the presence of time-varying selection bias and time-invariant heterogeneity. To satisfy the order and rank conditions for identification with multiple endogeneity, we propose lagged values of each treatment as excluded instruments. We evaluate the validity of the IV estimation assumptions on instrument relevance and exogeneity. Results indicate that the IV-GMM model offers enhanced control over selection bias and heterogeneity, and more importantly the panel data framework can provide valid excluded instruments that satisfy the order and rank conditions for identification when dealing with multiple endogenous variables. The approach outlined in this article has broad application for comparative effectiveness and health technology assessment involving multiple treatments/interventions using real-world nonexperimental data. Copyright © 2012 John Wiley & Sons, Ltd.