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Published on: January 8, 2020
Instrumental variable analysis to estimate treatment effects: a simulation study showing potential benefits of
I E Ceyisakar1, N van Leeuwen2, E W Steyerberg2,3
1Centre for Medical Decision Making, Department of Public Health, Erasmus MC - University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands. iris.kohen@gmail.com.
Background:
Instrumental variable (IV) analysis holds the potential to estimate treatment effects from observational data. IV analysis potentially circumvents unmeasured confounding but makes a number of assumptions, such as that the IV shares no common cause with the outcome. When using treatment preference as an instrument, a common cause, such as a preference regarding related treatments, may exist. We aimed to explore the validity and precision of a variant of IV analysis where we additionally adjust for the provider: adjusted IV analysis.
Methods:
A treatment effect on an ordinal outcome was simulated (beta - 0.5 in logistic regression) for 15.000 patients, based on a large data set (the IMPACT data, n = 8799) using different scenarios including measured and unmeasured confounders, and a common cause of IV and outcome. We compared estimated treatment effects with patient-level adjustment for confounders, IV with treatment preference as the instrument, and adjusted IV, with hospital added as a fixed effect in the regression models.
Results:
The use of patient-level adjustment resulted in biased estimates for all the analyses that included unmeasured confounders, IV analysis was less confounded, but also less reliable. With correlation between treatment preference and hospital characteristics (a common cause) estimates were skewed for regular IV analysis, but not for adjusted IV analysis.
Conclusion:
When using IV analysis for comparing hospitals, some limitations of regular IV analysis can be overcome by adjusting for a common cause.
Trial Registration:
We do not report the results of a health care intervention.
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