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Falsification Tests for Instrumental Variable Designs With an Application to Tendency to Operate

Keele, Luke, PhD*; Zhao, Qingyuan, PhD; Kelz, Rachel R., MD, MSc; Small, Dylan, PhD

doi: 10.1097/MLR.0000000000001040
Statistical Workshop
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Background: Instrumental variable (IV) methods are becoming an increasingly important tool in health services research as they can provide consistent estimates of causal effects in the presence of unobserved confounding. However, investigators must provide justifications that the IV is independent with any unmeasured confounder and its effect on the outcome occurs only through receipt of the exposure. These assumptions, while plausible in some contexts, cannot be verified from the data.

Methods: Falsification tests can be applied to provide evidence for the key IV assumptions. A falsification test cannot prove the assumptions hold, but can provide decisive evidence when the assumption fails. We provide a general overview of falsification tests for IV designs. We highlight a falsification test that utilizes a subpopulation of the data where an overwhelming proportion of units are treated or untreated. If the IV assumptions hold, we should find the intention-to-treat effect is zero within these subpopulations.

Results: We demonstrate the usage of falsification tests for IV designs using an IV known as tendency to operate from health services research. We show that the falsification test provides no evidence against the IV assumptions in this application.

Departments of *Surgery

Statistics, University of Pennsylvania

Department of Surgery, Center for Surgery and Health Economics, Hospital of the University of Pennsylvania, Philadelphia, PA

R.R.K. is funded by a grant from the National Institute on Aging, R01AG049757-01A1. The dataset used for this study was purchased with a grant from the Society of American Gastrointestinal and Endoscopic Surgeons.

The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Although the AMA Physician Masterfile data are the source of the raw physician data, the tables and tabulations were prepared by the authors and do not reflect the work of the AMA. The Pennsylvania Health Cost Containment Council (PHC4) is an independent state agency responsible for addressing the problems of escalating health costs, ensuring the quality of health care, and increasing access to health care for all citizens. While PHC4 has provided data for this study, PHC4 specifically disclaims responsibility for any analyses, interpretations or conclusions.

The authors declare no conflict of interest.

Reprints: Luke Keele, PhD, McCourt School of Public Policy, Georgetown University, 37th & O Street, NW, Washington, DC 20057. E-mail: luke.keele@gmail.com.

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