Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China.
Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China.
BMC Med Res Methodol. 2023 Oct 23;23(1):247. doi: 10.1186/s12874-023-02065-6.
When estimating the causal effect on survival outcomes in observational studies, it is necessary to adjust confounding factors due to unbalanced covariates between treatment and control groups. There is no study on multiple robust method for estimating the difference in survival functions. In this study, we propose a multiply robust (MR) estimator, allowing multiple propensity score models and outcome regression models, to provide multiple protection.
Based on the previous MR estimator (Han 2014) and pseudo-observation approach, we proposed a new MR estimator for estimating the difference in survival functions. The proposed MR estimator based on the pseudo-observation approach has several advantages. First, the proposed estimator has a small bias when any PS and OR models were correctly specified. Second, the proposed estimator considers the advantage pf the pseudo-observation approach, which avoids proportional hazards assumption. A Monte Carlo simulation study was performed to evaluate the performance of the proposed estimator. And the proposed estimator was used to estimate the effect of chemotherapy on triple-negative breast cancer (TNBC) in real data.
The simulation studies showed that the bias of the proposed estimator was small, and the coverage rate was close to 95% when any model for propensity score or outcome regression is correctly specified regardless of whether the proportional hazard assumption holds, finite sample size and censoring rate. And the simulation results also showed that even though the propensity score models are misspecified, the bias of the proposed estimator was still small when there is a correct model in candidate outcome regression models. And we applied the proposed estimator in real data, finding that chemotherapy could improve the prognosis of TNBC.
The proposed estimator, allowing multiple propensity score and outcome regression models, provides multiple protection for estimating the difference in survival functions. The proposed estimator provided a new choice when researchers have a "difficult time" choosing only one model for their studies.
在观察性研究中估计生存结局的因果效应时，由于处理组和对照组之间的协变量不平衡，需要调整混杂因素。目前还没有研究提出同时适用于多个倾向评分模型和结局回归模型的稳健估计方法来估计生存函数的差异。本研究提出了一种多重稳健（MR）估计量，允许使用多个倾向评分模型和结局回归模型，提供多重保护。
基于之前的 MR 估计量（Han 2014）和拟似观测方法，我们提出了一种新的 MR 估计量，用于估计生存函数的差异。基于拟似观测方法的新 MR 估计量具有几个优点。首先，当任何 PS 和 OR 模型都正确指定时，该估计量具有较小的偏差。其次，该估计量考虑了拟似观测方法的优势，避免了比例风险假设。通过蒙特卡罗模拟研究来评估所提出估计量的性能。并将所提出的估计量应用于真实数据中估计化疗对三阴性乳腺癌（TNBC）的影响。
模拟研究表明，无论是否满足比例风险假设、样本量有限和删失率，当任何倾向评分或结局回归模型正确指定时，所提出的估计量的偏差较小，覆盖率接近 95%。模拟结果还表明，即使倾向评分模型被错误指定，只要候选结局回归模型中有正确的模型，所提出的估计量的偏差仍然很小。我们将所提出的估计量应用于真实数据，发现化疗可以改善 TNBC 的预后。
允许使用多个倾向评分和结局回归模型的新提出的估计量为估计生存函数的差异提供了多重保护。当研究人员难以仅为研究选择一个模型时，该估计量提供了一个新的选择。
