Yang Fan, Cheng Jing, Huo Dezheng
Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, USA.
Department of Preventive and Restorative Dental Sciences, University of California San Francisco, San Francisco, California 94118, USA.
Obs Stud. 2019;5:141-162. doi: 10.1353/obs.2019.0008. Epub 2019 Oct 18.
The use of postmastectomy radiotherapy (PMRT) on women with AJCC (American Joint Committee on Cancer) pT1-2pN1 breast cancer is controversial in practice. Huo et al. (2015) found that PMRT was associated with longer survival among a high-risk subgroup of AJCC pT1-2pN1 patients using a Cox model on data from the National Cancer Database. To address unmeasured confounding in this observational study, we consider the variation among facilities in the use of PMRT as an instrumental variable (IV). Recently, there has been widespread use of the two-stage residual inclusion (2SRI) method offered by Terza et al. (2008) for nonlinear models, and 2SRI has been the method of choice for analyzing proportional hazards model using IV in clinical studies. However, the causal parameter using 2SRI is only identified under a homogeneity assumption that goes beyond the standard assumptions of IV, and Wan et al. (2015) demonstrated that under standard IV assumptions, 2SRI could fail to consistently estimate the causal hazard ratio for compliers. In this paper, following Yu et al. (2015), we apply a model-based IV approach (Imbens and Rubin, 1997; Hirano et al., 2000) which allows consistent estimation of the causal hazard ratio for survival outcomes with a proportional hazards model specification under standard IV assumptions while flexibly incorporating the restrictions imposed by IV assumptions. Simulation studies show that when there is unmeasured confounding, both 2SRI and the standard Cox regression could provide biased estimates of the causal hazard ratio among compliers, while this model-based IV approach provides consistent estimates. We apply this IV method to the breast cancer study and our IV analysis did not find strong evidence to support the benefit of PMRT on survival among the targeted patients. In addition, we develop sensitivity analysis approaches to assess the sensitivity of causal conclusions to violations of the exclusion restrictions assumption for IV.
对美国癌症联合委员会（AJCC）pT1 - 2pN1期乳腺癌女性患者使用乳房切除术后放疗（PMRT）在实际应用中存在争议。 Huo等人（2015年）利用国家癌症数据库的数据，通过Cox模型发现，在AJCC pT1 - 2pN1患者的一个高危亚组中，PMRT与更长的生存期相关。为了解决这项观察性研究中未测量的混杂因素，我们将PMRT使用方面各机构之间的差异视为一个工具变量（IV）。最近，Terza等人（2008年）提出的两阶段残差纳入（2SRI）方法在非线性模型中得到了广泛应用，并且2SRI已成为临床研究中使用IV分析比例风险模型的首选方法。然而，使用2SRI的因果参数仅在一个超出IV标准假设的同质性假设下才能识别，并且Wan等人（2015年）证明，在标准IV假设下，2SRI可能无法一致地估计依从者的因果风险比。在本文中，我们遵循Yu等人（2015年）的方法，应用基于模型的IV方法（Imbens和Rubin，1997年；Hirano等人，2000年），该方法允许在标准IV假设下，通过比例风险模型规范一致地估计生存结局的因果风险比，同时灵活纳入IV假设所施加的限制。模拟研究表明，当存在未测量的混杂因素时，2SRI和标准Cox回归都可能对依从者的因果风险比提供有偏差的估计，而这种基于模型的IV方法提供一致的估计。我们将这种IV方法应用于乳腺癌研究，并且我们的IV分析没有发现有力证据支持PMRT对目标患者生存有益。此外，我们开发了敏感性分析方法，以评估因果结论对IV排除限制假设违反情况的敏感性。
