Instrumental Variables and Heterogeneous Treatment Effects

Maciejewski et al discuss instrumental variable analysis, a method designed to reduce or eliminate unobserved confounding in observational studies, with the goal of achieving unbiased estimation of treatment effects. A randomized clinical trial (RCT) can be used to estimate the average treatment eff...

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Bibliographic Details
Published in:JAMA : the journal of the American Medical Association 2022-03, Vol.327 (12), p.1177-1178
Main Authors: Maciejewski, Matthew L, Dowd, Bryan E, Norton, Edward C
Format: Article
Language:English
Subjects:
Heterogeneity
Humans
Models, Statistical
Patients
Randomized Controlled Trials as Topic - statistics & numerical data
Statistical analysis
Subgroups
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Summary:Maciejewski et al discuss instrumental variable analysis, a method designed to reduce or eliminate unobserved confounding in observational studies, with the goal of achieving unbiased estimation of treatment effects. 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-aver-age treatment effect. Subgroup analyses often are used to evaluate heterogeneity in the treatment effect. 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. 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.
ISSN:0098-7484
1538-3598
DOI:10.1001/jama.2022.2505