PURPOSE Observational studies using routinely collected data are faced with a number of potential shortcomings that can bias their results. Many methods rely on controlling for measured and unmeasured confounders. In this work, we investigate the use of instrumental variables (IV) and quasi-trial analysis to control for unmeasured confounders in the context of a study based on the retrospective Epidemiological Strategy and Medical Economics (ESME) database, which compared overall survival (OS) with paclitaxel plus bevacizumab or paclitaxel alone as first-line treatment in patients with HER2-negative metastatic breast cancer (MBC). PATIENTS AND METHODS Causal interpretations and estimates can be made from observation data using IV and quasi-trial analysis. Quasi-trial analysis has the same conceptual basis as IV, however, instead of using IV in the analysis, a "superficial" or "pseudo" randomized trial is used in a Cox model. For instance, in a multicenter trial, instead of using the treatment variable, quasi-trial analysis can consider the treatment preference in each center, which can be informative, and then comparisons of results between centers or clinicians can be informative. RESULTS In the original analysis, the OS adjusted for major factors was significantly longer with paclitaxel and bevacizumab than with paclitaxel alone. Using the center-treatment preference as an instrument yielded to concordant results. For the quasi-trial analysis, a Cox model was used, adjusted on all factors initially used. The results consolidate those obtained with a conventional multivariate Cox model. CONCLUSION Unmeasured confounding is a major concern in observational studies, and IV or quasi-trial analysis can be helpful to complement analysis of studies of this nature.

中文翻译：

---

解决观察性研究中的偏倚问题：在 ESME 研究项目中使用工具变量和准随机试验。

目的 使用常规收集数据的观察性研究面临着许多可能使结果产生偏差的潜在缺陷。许多方法依赖于控制测量和未测量的混杂因素。在这项工作中，我们调查了在一项基于回顾性流行病学策略和医学经济学 (ESME) 数据库的研究中使用工具变量 (IV) 和准试验分析来控制未测量的混杂因素，该数据库比较了总生存期 (OS ) 紫杉醇联合贝伐单抗或单独紫杉醇作为 HER2 阴性转移性乳腺癌 (MBC) 患者的一线治疗。患者和方法 可以使用 IV 和准试验分析从观察数据中做出因果解释和估计。准试验分析具有与 IV 相同的概念基础，但是，在分析中不使用 IV，而是在 Cox 模型中使用“表面”或“伪”随机试验。例如，在多中心试验中，准试验分析可以考虑每个中心的治疗偏好，而不是使用治疗变量，这可以提供信息，然后中心或临床医生之间的结果比较可以提供信息。结果 在最初的分析中，根据主要因素调整后的 OS 使用紫杉醇和贝伐珠单抗比单独使用紫杉醇显着更长。使用中心治疗偏好作为工具产生了一致的结果。对于准试验分析，使用了 Cox 模型，并根据最初使用的所有因素进行了调整。结果巩固了使用传统多变量 Cox 模型获得的结果。