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A randomized clinical trial (RCT) can be used to estimate the average treatment effect for a population. Some patients experience a treatment effect that is larger than the average, while others experience a smaller-than-average treatment effect. Subgroup analyses often are used to evaluate heterogeneity in the treatment effect.1 When it is infeasible or unethical to randomize patients to a treatment, the average treatment effect may be a combination of the true treatment effect and the effects of confounders—factors that influence both the treatment selected and patient outcomes.2 When confounding factors are unknown or unobserved, correcting for their effect in statistical analyses is challenging. Instrumental variable analysis is one approach to address unobserved confounding.
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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.
Corresponding Author: Matthew L. Maciejewski, PhD, Department of Population Health Sciences, Duke University Medical Center, 508 Fulton St, Ste 600, Durham, NC 27705 (email@example.com).
Conflict of Interest Disclosures: Dr Maciejewski reported receiving grants from US Department of Veterans Affairs Health Services Research & Development (RCS 10-391) and that he owns Amgen stock through his spouse's employment. No other disclosures were reported.
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