Alexander Joe, Edwards Roger A, Savoldelli Alberto, Manca Luigi, Grugni Roberto, Emir Birol, Whalen Ed, Watt Stephen, Brodsky Marina, Parsons Bruce
Pfizer Inc, 235 E 42nd St, New York, NY, 10017, USA.
Health Services Consulting Corporation, 169 Summer Road, Boxborough, MA, 01719, USA.
BMC Med Res Methodol. 2017 Jul 20;17(1):113. doi: 10.1186/s12874-017-0389-2.
More patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN).
We utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores.
Cluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients.
The combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.
随着对患者对医疗治疗反应差异的了解不断深入，人们期望提供更具个性化的医疗护理。分析工具通过将来自不同类型研究（如随机对照试验（RCT）和观察性研究）的治疗反应联系起来，从而提供深入见解。鉴于这两种研究证据的重要性，我们的目标是将这些类型的数据整合到一个单一的预测平台中，以帮助预测个体疼痛性糖尿病周围神经病变（pDPN）患者对普瑞巴林的反应。
我们利用了三项普瑞巴林的关键RCT（398名北美患者）和最大的普瑞巴林观察性研究（3159名德国患者）。我们实施了分层聚类分析，以在观察性研究中识别患者聚类，然后使用粗化精确匹配（CEM）技术将RCT患者与这些聚类进行匹配，从而创建一个匹配数据集。然后，我们使用最大似然法开发自回归移动平均模型（ARMAX），以估计匹配数据集中每个聚类中接受普瑞巴林治疗患者的每周疼痛评分。最后，我们使用未与RCT患者匹配的观察性研究患者，通过比较观察到的和预测的疼痛评分之间的t检验，对ARMAX模型进行验证。
聚类分析产生了六个聚类（每个聚类287 - 777名患者），聚类变量如下：性别、年龄、pDPN病程、体重指数、抑郁病史、普瑞巴林单药治疗、既往加巴喷丁使用情况、基线疼痛评分和基线睡眠干扰。CEM在匹配数据集中产生了1528名独特患者。添加RCT患者后，聚类的全局不平衡评分降低（根据聚类不同，降低幅度在6%至63%之间），这表明该过程减少了六个聚类中五个聚类的协变量偏差。疼痛评分的ARMAX模型表现良好（R：0.85 - 0.91；均方根误差：0.53 - 0.57）。在1955名未与RCT患者匹配的患者中，t检验未显示观察到的和预测的疼痛评分之间存在差异。
聚类分析、CEM和ARMAX建模的结合在疼痛评分方面具有强大的预测能力。使用CEM整合RCT和观察性研究数据能够有效利用观察性研究数据来预测患者反应。
