  Instrumental Variables provide a way of addressing bias due to unmeasured
confounding when estimating treatment effects using observational data. As
instrument prescription preference of individual healthcare providers has been
proposed. Because prescription preference is hard to measure and often
unobserved, a surrogate measure constructed from available data is often
required for the analysis. Different construction methods for this surrogate
measure are possible, such as simple rule-based methods which make use of the
observed treatment patterns, or more complex model-based methods that employ
formal statistical models to explain the treatment behaviour whilst considering
measured confounders. The choice of construction method relies on aspects like
data availability within provider, missing data in measured confounders, and
possible changes in prescription preference over time. In this paper we conduct
a comprehensive simulation study to evaluate different construction methods for
surrogates of prescription preference under different data conditions,
including: different provider sizes, missing covariate data, and change in
preference. We also propose a novel model-based construction method to address
between provider differences and change in prescription preference. All
presented construction methods are exemplified in a case study of the relative
glucose lowering effect of two type 2 diabetes treatments in observational
data. Our study shows that preference-based Instrumental Variable methods can
be a useful tool for causal inference from observational health data. The
choice of construction method should be driven by the data condition at hand.
Our proposed method is capable of estimating the causal treatment effect
without bias in case of sufficient prescription data per provider, changing
prescription preference over time and non-ignorable missingness in measured
confounders.

Methodology