Two stage least squares with time-varying instruments: An application to an evaluation of treatment intensification for type-2 diabetes.

As routinely collected longitudinal data becomes more available in many settings, policy makers are increasingly interested in the effect of time-varying treatments (sustained treatment strategies). In settings such as this, many commonly used statistical approaches for estimating treatment effects, such as g-methods, often adopt the 'no unmeasured confounding' assumption. Instrumental variable (IV) methods aim to reduce biases due to unmeasured confounding, but have received limited attention in settings with time-varying treatments. This paper extends and critically evaluates a commonly used IV estimating approach, Two Stage Least Squares (2SLS), for evaluating time-varying treatments. Using a simulation study, we found that, unlike standard 2SLS, the extended 2SLS performs relatively well across a wide range of circumstances, including certain model misspecifications. We illustrate the methods in an evaluation of treatment intensification for Type-2 Diabetes Mellitus, exploring the exogeneity in prescribing preferences to operationalise a time-varying instrument.