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Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results

Author

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  • Brookhart M. Alan

    (Division of Pharmacoepidemiology, Brigham and Women’s Hospital & Harvard Medical School)

  • Schneeweiss Sebastian

    (Division of Pharmacoepidemiology, Brigham and Women’s Hospital & Harvard Medical School)

Abstract

Observational studies of prescription medications and other medical interventions based on administrative data are increasingly used to inform regulatory and clinical decision making. The validity of such studies is often questioned, however, because the available data may not contain measurements of important prognostic variables that guide treatment decisions. Recently, approaches to this problem have been proposed that use instrumental variables (IV) defined at the level of an individual health care provider or aggregation of providers. Implicitly, these approaches attempt to estimate causal effects by using differences in medical practice patterns as a quasi-experiment. Although preference-based IV methods may usefully complement standard statistical approaches, they make assumptions that are unfamiliar to most biomedical researchers and therefore the validity of such an analysis can be hard to evaluate. Here, we describe a simple framework based on a single unobserved dichotomous variable that can be used to explore how violations of IV assumptions and treatment effect heterogeneity may bias the standard IV estimator with respect to the average treatment effect in the population. This framework suggests various ways to anticipate the likely direction of bias using both empirical data and commonly available subject matter knowledge, such as whether medications or medical procedures tend to be overused, underused, or often misused. This approach is described in the context of a study comparing the gastrointestinal bleeding risk attributable to different non-steroidal anti-inflammatory drugs.

Suggested Citation

  • Brookhart M. Alan & Schneeweiss Sebastian, 2007. "Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-25, December.
  • Handle: RePEc:bpj:ijbist:v:3:y:2007:i:1:n:14
    DOI: 10.2202/1557-4679.1072
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    Citations

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    Cited by:

    1. Byeong Yeob Choi, 2021. "Instrumental variable estimation of truncated local average treatment effects," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-12, April.
    2. Roderick J. Little & Qi Long & Xihong Lin, 2009. "A Comparison of Methods for Estimating the Causal Effect of a Treatment in Randomized Clinical Trials Subject to Noncompliance," Biometrics, The International Biometric Society, vol. 65(2), pages 640-649, June.
    3. M Bilal Iqbal & Simon D Robinson & Lillian Ding & Anthony Fung & Eve Aymong & Albert W Chan & Steven Hodge & Anthony Della Siega & Imad J Nadra & British Columbia Cardiac Registry Investigators, 2016. "Intra-Aortic Balloon Pump Counterpulsation during Primary Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction and Cardiogenic Shock: Insights from the British Columbia Cardiac Re," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-14, February.
    4. Douglas Lehmann & Yun Li & Rajiv Saran & Yi Li, 2017. "Strengthening Instrumental Variables Through Weighting," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 320-338, December.
    5. Anirban Basu & Kwun Chuen Gary Chan, 2014. "Can We Make Smart Choices Between Ols And Contaminated Iv Methods?," Health Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 462-472, April.
    6. Anirban Basu & Anupam B. Jena & Dana P. Goldman & Tomas J. Philipson & Robert Dubois, 2014. "Heterogeneity In Action: The Role Of Passive Personalization In Comparative Effectiveness Research," Health Economics, John Wiley & Sons, Ltd., vol. 23(3), pages 359-373, March.
    7. Fan Yang & José R. Zubizarreta & Dylan S. Small & Scott Lorch & Paul R. Rosenbaum, 2014. "Dissonant Conclusions When Testing the Validity of an Instrumental Variable," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 253-263, November.
    8. Xuran Wang & Yang Jiang & Nancy R. Zhang & Dylan S. Small, 2018. "Sensitivity analysis and power for instrumental variable studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1150-1160, December.
    9. Ertefaie Ashkan & Small Dylan & Flory James & Hennessy Sean, 2016. "Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 219-232, May.
    10. Jaeun Choi & A. James O'Malley, 2017. "Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 159-185, January.
    11. Robert Carroll & Chris Metcalfe & Sarah Steeg & Neil M Davies & Jayne Cooper & Nav Kapur & David Gunnell, 2016. "Psychosocial Assessment of Self-Harm Patients and Risk of Repeat Presentation: An Instrumental Variable Analysis Using Time of Hospital Presentation," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.

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