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Instrumental Variable Analysis

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Health Services Evaluation

Part of the book series: Health Services Research ((HEALTHSR))

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Abstract

A goal of many health services research studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead an observational study must be used. A major difficulty with observational studies is that there might be unmeasured confounding, i.e., unmeasured ways in which the treatment and control groups differ before treatment that affect the outcome. Instrumental variable analysis is a method for controlling for unmeasured confounding. Instrumental variable analysis requires the measurement of a valid instrumental variable, which is a variable that is independent of the unmeasured confounding and encourages a subject to take one treatment level versus another, while having no effect on the outcome beyond its encouragement of a certain treatment level. This chapter discusses the types of causal effects that can be estimated by instrumental variable analysis, the assumptions needed for instrumental variable analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions, methods of estimation of causal effects using instrumental variables, and sources of instrumental variables in health services research studies.

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Acknowledgments

Jing Cheng and Dylan Small were supported by grant RC4MH092722 from the National Institute of Mental Health. The authors thank Scott Lorch for the use of the data from the NICU study.

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Authors and Affiliations

  1. Department of Statistics, Stanford University, Stanford, CA, USA

    Michael Baiocchi

  2. Department of Preventive and Restorative Dental Sciences, University of California, San Francisco School of Dentistry, San Francisco, CA, USA

    Jing Cheng

  3. University of Pennsylvania, Philadelphia, PA, USA

    Dylan S. Small

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  1. Michael Baiocchi
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  2. Jing Cheng
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  3. Dylan S. Small
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Correspondence to Michael Baiocchi .

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Editors and Affiliations

  1. Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada

    Adrian Levy

  2. ICON plc, Vancouver, BC, Canada

    Sarah Goring

  3. Department of Biostatistics, Brown University, Providence, RI, USA

    Constantine Gatsonis

  4. University of British Columbia, Vancouver, BC, Canada

    Boris Sobolev

  5. European Observatory on Health Systems and Policies, Department of Health Care Management, Berlin University of Technology, Berlin, Germany

    Ewout van Ginneken

  6. Department Health Care Management Faculty of Economics and Management, Technische Universität Berlin, Berlin, Germany

    Reinhard Busse

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Baiocchi, M., Cheng, J., Small, D.S. (2019). Instrumental Variable Analysis. In: Levy, A., Goring, S., Gatsonis, C., Sobolev, B., van Ginneken, E., Busse, R. (eds) Health Services Evaluation. Health Services Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8715-3_32

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