Yang Eric, O'Donovan Christopher, Phillips JodiLyn, Atkinson Leone, Ghosh Krishnendu, Agrafiotis Dimitris K
Covance Inc., 210 Carnegie Center, Princeton, NJ 08540, USA.
Contemp Clin Trials Commun. 2018 Jan 31;9:108-114. doi: 10.1016/j.conctc.2018.01.005. eCollection 2018 Mar.
One of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity can complicate interpretation. Here, we demonstrate how operational data derived from central laboratory services can provide key insights into the performance of clinical sites and help guide operational planning and site selection for new clinical trials.
Our methodology uses the metadata associated with laboratory kit shipments to clinical sites (such as trial and anonymized patient identifiers, investigator names and addresses, sample collection and shipment dates, etc.) to reconstruct the complete schedule of patient visits and derive insights about the operational performance of those sites, including screening, enrollment, and drop-out rates and other quality indicators. This information can be displayed in its raw form or normalized to enable direct comparison of site performance across studies of varied design and complexity.
Leveraging Covance's market leadership in central laboratory services, we have assembled a database of operational metrics that spans more than 14,000 protocols, 1400 indications, 230,000 unique investigators, and 23 million patient visits and represents a significant fraction of all clinical trials run globally in the last few years. By analyzing this historical data, we are able to assess and compare the performance of clinical investigators across a wide range of therapeutic areas and study designs. This information can be aggregated across trials and geographies to gain further insights into country and regional trends, sometimes with surprising results.
The use of operational data from Covance Central Laboratories provides a unique perspective into the performance of clinical sites with respect to many important metrics such as patient enrollment and retention. These metrics can, in turn, be used to guide operational planning and site selection for new clinical trials, thereby accelerating recruitment, improving quality, and reducing cost.
开展一项成功的临床试验的关键之一是选择高质量的临床研究点,即能够迅速招募患者、持续吸引患者以防止其退出,并严格按照临床方案执行试验的研究点。直观地说,一个研究点的历史记录是其未来表现的最强预测指标之一;然而,数据可用性以及方案复杂性差异巨大等问题会使解读变得复杂。在此,我们展示了源自中央实验室服务的运营数据如何能为临床研究点的表现提供关键见解,并有助于指导新临床试验的运营规划和研究点选择。
我们的方法利用与运往临床研究点的实验室试剂盒运输相关的元数据(如试验和匿名患者标识符、研究者姓名和地址、样本采集和运输日期等)来重建患者就诊的完整时间表,并得出有关这些研究点运营表现的见解,包括筛选、入组和退出率以及其他质量指标。此信息可以原始形式显示,也可以进行归一化处理,以便在不同设计和复杂性的研究中直接比较研究点的表现。
凭借科文斯在中央实验室服务方面的市场领先地位,我们汇集了一个运营指标数据库,该数据库涵盖超过14000个方案、1400种适应症、230000名不同的研究者以及2300万次患者就诊,代表了过去几年全球开展的所有临床试验的很大一部分。通过分析这些历史数据,我们能够评估和比较广泛治疗领域和研究设计中的临床研究者的表现。这些信息可以跨试验和地区进行汇总,以进一步洞察国家和地区趋势,有时会得出令人惊讶的结果。
使用来自科文斯科文斯中央实验室的运营数据,能为临床研究点在患者入组和留存等许多重要指标方面的表现提供独特视角。反过来,这些指标可用于指导新临床试验的运营规划和研究点选择,从而加快招募速度、提高质量并降低成本。