Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies

项目概要/摘要 风湿性关节炎（RA）是最常见的自身免疫性关节疾病，有超过15种治疗选择， 反映了治疗的进展以及对治疗的异质性反应。在第一行之后 治疗甲氨蝶呤（MTX），患者和他们的风湿病学家进行试验和错误的方法，以确定 最佳治疗。一项具有里程碑意义的随机对照临床试验（RCT）RACAT比较了 氨甲喋呤、柳氮磺吡啶和羟氯喹三联疗法与氨甲喋呤和肿瘤坏死因子联合治疗疗效比较 抑制剂（TNFi）。RACAT亚组分析观察到，一些患者对一种药物的反应更好， 治疗策略与其他然而，与大多数随机对照试验一样， 分组。真实世界数据（RWD），如电子健康记录（EHR）和注册数据，具有较大的 样本量，但缺乏随机化和精确的临床测量作为临床试验的一部分。 本提案的目的是应用并严格测试最先进的方法，这些方法可以将联合收割机 RCT和RWD的优势，以扩展RCT结果。RACAT是退伍军人事务部（VA）的临床试验 因此，他们的许多研究对象也有并行的EHR数据，为测试方法提供了理想的研究设计 来了解我们使用RWD复制RCT的效果。在目标1中，我们使用半监督的 使用EHR数据插补RACAT临床终点的机器学习方法;相关RACAT数据将 作为黄金标准比较。接下来，我们应用因果推理模型比较三联疗法与 TNFi使用EHR数据和插补终点，并使用链接的RACAT数据验证结果。在目标2中， 我们应用了新的因果建模方法，使我们能够使用RWD检查亚组结果。我们将 识别大型EHR和与RACAT亚组相似的登记研究中的受试者，即受益的患者 三联疗法与TNFi相比或相反，在整个试验期间保持TNFi治疗并 好.这些更大的人群将为研究治疗反应的潜在预测因素提供更高的把握度。 此外，EHR数据的整合使我们能够研究一组更广泛的潜在预测因素，而这些预测因素在 RCT或登记数据。我们的总体假设是，我们将确定在研究中观察到的临床亚组。 在EHR和登记研究中，RACAT在更大的RA患者人群中对治疗的反应不同。 我们还将通过使用EHR中可用的更广泛的临床数据集来确定新的反应预测因子。这 这项研究意义重大，因为它将为在数据集中扩展RCT结果的研究提供蓝图， RCT和RWD相关，适用于多种治疗和疾病。这项研究之所以具有创新性， 使用关联数据集最大化RCT中可用数据的方法，使用现有RWD，为研究提供动力 为不同的患者优化RA治疗。该提案还预计患者的能力将不断提高， 机构访问EHR数据，使以前孤立的数据集成为数据驱动研究的一部分， 先进的RA和其他疾病的临床管理。

PROJECT SUMMARY/ABSTRACT Rheumatoid arthritis (RA) is the most common autoimmune joint disease with over 15 treatment options, reflecting both advances in therapy as well as the heterogenous response to therapy. After the first line therapy methotrexate (MTX), patients and their rheumatologist proceed on a trial-and-error approach to identify the optimal treatment. A landmark randomized controlled clinical trial (RCT), RACAT, compared the effectiveness of triple therapy-MTX, sulfasalazine, and hydroxychloroquine vs MTX and a tumor necrosis factor inhibitor (TNFi). The RACAT subgroup analyses observed that some patients had a better response to one treatment strategy vs the other. However, like most RCTs, it was underpowered to better characterize these subgroups. Real-world data (RWD), such as electronic health record (EHR) and registry data, have a larger sample size but lack the randomization and precise clinical measurements performed as part of clinical trials. The objective of this proposal is to apply and rigorously test state-of-the-art methods that can combine the strengths of RCT and RWD to extend RCT findings. RACAT was a Veterans Affairs (VA) based clinical trial and thus many of their subjects also have EHR data in parallel, providing an ideal study design to test methods to understand how well we can replicated RCT using RWD. In Aim 1, we test methods using semi-supervised machine learning methods to impute RACAT clinical endpoints using EHR data; the linked RACAT data will be used as the gold standard comparison. Next, we apply causal inference modeling comparing triple therapy vs TNFi using EHR data with the imputed endpoints and validate results using the linked RACAT data. In Aim 2, we apply novel causal modeling methods that enable us to examine subgroup findings using RWD. We will identify subjects in the larger EHR and registries similar to RACAT subgroups, i.e. patients who benefitted more from triple therapy vs TNFi or vice versa, and subjects who remained on TNFi throughout the trial and did well. These larger populations will provide improved power to study potential predictors of treatment response. Moreover, the integration of EHR data allows us to study a broader set of potential predictors not collected in RCT or registry data. Our overarching hypothesis is that we will identify the clinical subgroups observed in RACAT with differing response to treatments within the larger populations of RA patients in EHR and registry. We will also identify novel predictors of response by using a broader set of clinical data available in EHR. This study is significant because it will provide a blueprint for studies for extending RCT findings in datasets with linked RCT and RWD, applicable to many treatments and conditions. This study is innovative because of its approach to maximize the data available from RCTs with existing RWD using linked datasets, powering studies to optimize RA therapy for different patients. This proposal also anticipates the growing ability of patients and institutions to access EHR data, enabling previously siloed datasets to become part of data-driven studies to advance clinical management of RA and other conditions.