As longitudinal data becomes more available in many settings, policy makers are increasingly interested in the effect of time-varying treatments (e.g. sustained treatment strategies). In settings such as this, the preferred analysis techniques are the g-methods, however these require the untestable assumption of no unmeasured confounding. Instrumental variable analyses can minimise bias through unmeasured confounding. Of these methods, the Two Stage Least Squares technique is one of the most well used in Econometrics, but it has not been fully extended, and evaluated, in full time-varying settings. This paper proposes a robust two stage least squares method for the econometric evaluation of time-varying treatment. Using a simulation study we found that, unlike standard two stage least squares, it performs relatively well across a wide range of circumstances, including model misspecification. It compares well with recent time-varying instrument approaches via g-estimation. 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.

翻译：随着纵向数据在许多情境中日益普及，政策制定者对时变治疗（例如持续治疗策略）的效果越来越感兴趣。在此类情境下，首选的分析方法是g-方法，但这些方法需要满足无法验证的无未测量混杂假设。工具变量分析能够通过未测量混杂最小化偏误。在这些方法中，两阶段最小二乘法是计量经济学中最常用的技术之一，但尚未在完整的时变情境中得到充分扩展和评估。本文提出了一种稳健的两阶段最小二乘法，用于时变治疗的计量经济学评估。通过模拟研究我们发现，与标准两阶段最小二乘法不同，该方法在包括模型误设在内的多种情境下均表现良好。与近期通过g-估计实现的时变工具变量方法相比具有优势。我们在2型糖尿病治疗强化评估中演示了该方法，通过探索处方偏好的外生性来实现时变工具变量的操作化。