Department Center for Observational Research, Amgen Ltd, Uxbridge, UK.
Department Science, Aetion, Inc, Boston, Massachusetts, USA.
Pharmacoepidemiol Drug Saf. 2021 Feb;30(2):248-256. doi: 10.1002/pds.5171. Epub 2020 Nov 21.
We evaluated the reproducibility of a study characterizing newly-diagnosed multiple myeloma (MM) patients within an electronic health records (EHR) database using different analytic tools.
We reproduced the findings of a descriptive cohort study using an iterative two-phase approach. In Phase I, a common protocol and statistical analysis plan (SAP) were implemented by independent investigators using the Aetion Evidence Platform® (AEP), a rapid-cycle analytics tool, and SAS statistical software as a gold standard for statistical analyses. Using the UK Clinical Practice Research Datalink (CPRD) dataset, the study included patients newly diagnosed with MM within primary care setting and assessed baseline demographics, conditions, drug exposure, and laboratory procedures. Phase II incorporated analysis revisions based on our initial comparison of the Phase I findings. Reproducibility of findings was evaluate by calculating the match rate and absolute difference in prevalence between the SAS and AEP study results.
Phase I yielded slightly discrepant results, prompting amendments to SAP to add more clarity to operational decisions. After detailed specification of data and operational choices, exact concordance was achieved for the number of eligible patients (N = 2646), demographics, comorbidities (i.e., osteopenia, osteoporosis, cardiovascular disease [CVD], and hypertension), bone pain, skeletal-related events, drug exposure, and laboratory investigations in the Phase II analyses.
In this reproducibility study, a rapid-cycle analytics tool and traditional statistical software achieved near-exact findings after detailed specification of data and operational choices. Transparency and communication of the study design, operational and analytical choices between independent investigators were critical to achieve this reproducibility.
我们评估了使用不同分析工具在电子健康记录（EHR）数据库中对新诊断多发性骨髓瘤（MM）患者进行研究的重现性。
我们采用迭代两阶段方法重现了一项描述性队列研究的结果。在第一阶段，独立研究者使用 Aetion Evidence Platform®（AEP），一种快速循环分析工具，以及 SAS 统计软件实施了共同的方案和统计分析计划（SAP），作为统计分析的金标准。使用英国临床实践研究数据链（CPRD）数据集，该研究包括在初级保健环境中新诊断为 MM 的患者，并评估了基线人口统计学、状况、药物暴露和实验室程序。第二阶段纳入了根据我们对第一阶段结果的初步比较进行的分析修订。通过计算 SAS 和 AEP 研究结果之间的匹配率和患病率的绝对差异来评估结果的重现性。
第一阶段产生了略有不同的结果，促使对 SAP 进行修订，以增加操作决策的清晰度。在详细规定数据和操作选择后，对于符合条件的患者数量（N=2646）、人口统计学、合并症（即骨质疏松症、骨质疏松症、心血管疾病[CVD]和高血压）、骨痛、骨骼相关事件、药物暴露和实验室研究，第二阶段分析中达到了完全一致。
在这项重现性研究中，经过详细规定数据和操作选择后，快速循环分析工具和传统统计软件得出了几乎完全一致的结果。独立研究者之间研究设计、操作和分析选择的透明度和沟通对于实现这种重现性至关重要。
