Robust Causal Comparisons of Nonrandomized Oncology Studies

肿瘤学研究中非随机分组的稳健因果比较 我们的研究目标是建立一个稳健的统计模型，用于非随机化组的因果比较。与 增加真实的世界数据（RWD）的可用性，试验设计比较新疗法与外部对照 通过RWD获得（例如，EHR）最近再次受到关注。该设计已用于治疗 特别是在RCT不可行的罕见疾病中，例如，3年无复发生存率（RFS） 接受辅助联合免疫治疗的局部治疗高危眼黑色素瘤患者与 在试验之外获得的同期对照。那么挑战就在于对是否存在因果关系做出推论 治疗有效延长（例如）患者生存率。但是用什么方法呢？已知因果推理依赖于 各种假设。尽管在使各种因果推理方法稳健方面取得了进展，例如，最值得注意的是 鲁棒估计（DRE）现有的DRE继续遭受几个主要缺点，例如，对温和型过于敏感 规格错误我们对增强型DRE的初步研究表明， 通过半参数模型对主要终点连续受限的试验进行推断。建立在此之上 开发，本申请的目标是开发用于分析非随机临床试验的新型DRE方法 二元（如肿瘤学缓解，流行病学发病率）和至事件发生时间（如生存和进展 肿瘤学中的无生存期）结果，并评估其统计特性，众所周知，这可能与 具有连续结果的原理案例的证明（目标1-2）。然后将这些方法应用于NCI赞助的试验， 一项人口科学研究和一项正在进行的免疫治疗试验。

Robust Causal Comparison of Nonrandomized Groups in Oncology Studies The goal of our research is to develop robust statistical models for causal comparison of nonrandomized groups. With the increasing availability of real world data (RWD), the trial design to compare a new therapy versus an external control obtained via RWD (e.g., EHR) has received renewed attention recently. This design has been utilized in therapeutic development especially in rare diseases when RCT is not feasible, e.g., comparing 3-year relapse-free survival (RFS) between locally treated high-risk ocular melanoma patients on adjuvant combination immunotherapy versus a matched contemporaneous control obtained outside of the trial. Then the challenge is at making causal inference on whether the treatment is efficacious in prolonging (e.g.) patient survival. But which method? Causal inference is known to depend on various assumptions. Despite advances in making various causal inference methods robust, e.g., most notably the doubly robust estimate (DRE) existing DREs continue to suffer several major drawbacks, e.g., being too sensitive to mild model misspecifications. Our preliminary studies on enhanced DREs that shows the needed robustness in making causal inference realized through semi-parametric models for trials limited with continuous primary endpoint. Built upon this development, the goal of this application is to develop the novel DRE approach for analyzing nonrandomized clinical trials with binary (such as response in oncology, incidence in epidemiology) and time to event (such as survival and progression free survival in oncology) outcomes and assess their statistical properties, which as is well known can be quite different from the proof of principle case with continuous outcome (Aims 1-2). The methods will then be applied to an NCI sponsored trial, and a population science study and one ongoing immunotherapy trial.