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Instrumental Variables provide a way of addressing bias due to unmeasuredconfounding when estimating treatment effects using observational data. Asinstrument prescription preference of individual healthcare providers has beenproposed. Because prescription preference is hard to measure and oftenunobserved, a surrogate measure constructed from available data is oftenrequired for the analysis. Different construction methods for this surrogatemeasure are possible, such as simple rule-based methods which make use of theobserved treatment patterns, or more complex model-based methods that employformal statistical models to explain the treatment behaviour whilst consideringmeasured confounders. The choice of construction method relies on aspects likedata availability within provider, missing data in measured confounders, andpossible changes in prescription preference over time. In this paper we conducta comprehensive simulation study to evaluate different construction methods forsurrogates of prescription preference under different data conditions,including: different provider sizes, missing covariate data, and change inpreference. We also propose a novel model-based construction method to addressbetween provider differences and change in prescription preference. Allpresented construction methods are exemplified in a case study of the relativeglucose lowering effect of two type 2 diabetes treatments in observationaldata. Our study shows that preference-based Instrumental Variable methods canbe a useful tool for causal inference from observational health data. Thechoice of construction method should be driven by the data condition at hand.Our proposed method is capable of estimating the causal treatment effectwithout bias in case of sufficient prescription data per provider, changingprescription preference over time and non-ignorable missingness in measuredconfounders.

Just what the doctor ordered: An evaluation of provider preference-based  Instrumental Variable methods in observational studies, with application for  comparative effectiveness of type 2 diabetes therapy