Robust Causal Comparisons of Nonrandomized Oncology Studies

非随机肿瘤学研究的稳健因果比较

基本信息

批准号：
10614590
负责人：
MING Tony TAN
金额：
$ 17.59万
依托单位：
GEORGETOWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-27 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10614590
关键词：
Acute Adjuvant Algorithms Attention Clinical Research Clinical Trials Combination immunotherapy Communities Data Databases Development Electronic Health Record Epidemiology Event Follow-Up Studies Goals Health Incidence Link Malignant Neoplasms Methods Modeling Observational Study Ocular Melanoma Oncology Outcome Patients Play Population Control Population Sciences Population Study Procedures Progression-Free Survivals Property Randomized, Controlled Trials Rare Diseases Relapse Research Sampling Statistical Models Subgroup Survival Rate Time Uveal Melanoma Work Yin comparison group design disease registry epidemiology study flexibility high risk immunotherapy trials insight meetings novel novel therapeutics primary endpoint programs randomized, clinical trials response semiparametric simulation smoking cessation therapeutic development translational study treatment effect trial comparing trial design tumor

项目摘要

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.

肿瘤学研究中非随机分组的稳健因果比较我们研究的目标是开发稳健的统计模型，用于非随机组的因果比较。随着增加真实世界数据（RWD）的可用性，这是比较新疗法与外部对照的试验设计通过 RWD（例如 EHR）获得的数据最近再次受到关注。该设计已用于治疗尤其是在随机对照试验不可行的罕见疾病中，例如比较 3 年无复发生存期 (RFS) 局部治疗的高危眼部黑色素瘤患者接受辅助联合免疫治疗与匹配治疗之间的比较在试验之外获得的同期控制。那么挑战在于对是否存在做出因果推断治疗可有效延长（例如）患者的生存期。但哪种方法呢？众所周知，因果推断取决于各种假设。尽管在使各种因果推理方法变得稳健方面取得了进步，例如，最值得注意的是双重因果推理方法稳健估计（DRE）现有的 DRE 仍然存在几个主要缺点，例如对温和模型过于敏感规格错误。我们对增强 DRE 的初步研究表明，建立因果关系所需的稳健性通过半参数模型实现的推断，仅限于连续主要终点的试验。在此基础上构建开发，该应用程序的目标是开发用于分析非随机临床试验的新型 DRE 方法具有二元性（例如肿瘤学中的反应、流行病学中的发病率）和事件发生时间（例如生存和进展）肿瘤学中的自由生存）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则案例的证明（目标 1-2）。这些方法随后将应用于 NCI 赞助的试验，以及一项人口科学研究和一项正在进行的免疫治疗试验。