Rostami Reza, Nahm Meredith, Pieper Carl F
Duke Clinical Research Institute, Duke University Medical Center, Durham, NC 27705, USA.
Clin Trials. 2009 Apr;6(2):141-50. doi: 10.1177/1740774509102590.
Despite a pressing and well-documented need for better sharing of information on clinical trials data quality assurance methods, many research organizations remain reluctant to publish descriptions of and results from their internal auditing and quality assessment methods.
We present findings from a review of a decade of internal data quality audits performed at the Duke Clinical Research Institute, a large academic research organization that conducts data management for a diverse array of clinical studies, both academic and industry-sponsored. In so doing, we hope to stimulate discussions that could benefit the wider clinical research enterprise by providing insight into methods of optimizing data collection and cleaning, ultimately helping patients and furthering essential research.
We present our audit methodologies, including sampling methods, audit logistics, sample sizes, counting rules used for error rate calculations, and characteristics of audited trials. We also present database error rates as computed according to two analytical methods, which we address in detail, and discuss the advantages and drawbacks of two auditing methods used during this 10-year period.
Our review of the DCRI audit program indicates that higher data quality may be achieved from a series of small audits throughout the trial rather than through a single large database audit at database lock. We found that error rates trended upward from year to year in the period characterized by traditional audits performed at database lock (1997-2000), but consistently trended downward after periodic statistical process control type audits were instituted (2001-2006). These increases in data quality were also associated with cost savings in auditing, estimated at 1000 h per year, or the efforts of one-half of a full time equivalent (FTE).
Our findings are drawn from retrospective analyses and are not the result of controlled experiments, and may therefore be subject to unanticipated confounding. In addition, the scope and type of audits we examine here are specific to our institution, and our results may not be broadly generalizable.
Use of statistical process control methodologies may afford advantages over more traditional auditing methods, and further research will be necessary to confirm the reliability and usability of such techniques. We believe that open and candid discussion of data quality assurance issues among academic and clinical research organizations will ultimately benefit the entire research community in the coming era of increased data sharing and re-use.
尽管迫切需要更好地共享临床试验数据质量保证方法的信息，且已有充分记录，但许多研究机构仍不愿公布其内部审计和质量评估方法的描述及结果。
我们展示了对杜克临床研究所（Duke Clinical Research Institute）进行的十年内部数据质量审计的回顾结果。该研究所是一家大型学术研究机构，为各类临床研究（包括学术性和行业资助的研究）进行数据管理。通过这样做，我们希望通过深入了解优化数据收集和清理的方法来激发讨论，这些讨论可能会使更广泛的临床研究事业受益，最终帮助患者并推动重要研究。
我们介绍了我们的审计方法，包括抽样方法、审计流程、样本量、用于错误率计算的计数规则以及被审计试验的特征。我们还展示了根据两种详细阐述的分析方法计算出的数据库错误率，并讨论了这十年间使用的两种审计方法的优缺点。
我们对杜克临床研究所以及项目的审查表明，在整个试验过程中进行一系列小规模审计，可能比在数据库锁定时进行一次大型数据库审计能实现更高的数据质量。我们发现，在以数据库锁定时进行的传统审计为特征的时期（1997 - 2000年），错误率逐年上升，但在采用定期统计过程控制类型审计后（2001 - 2006年）一直呈下降趋势。数据质量的这些提高还与审计成本的节省相关，估计每年节省1000小时，相当于半个全时当量（FTE）的工作量。
我们的发现来自回顾性分析，而非对照实验的结果，因此可能受到意外混杂因素的影响。此外，我们在此研究的审计范围和类型特定于我们的机构，我们的结果可能无法广泛推广。
与更传统的审计方法相比，使用统计过程控制方法可能具有优势，需要进一步研究以确认此类技术的可靠性和实用性。我们相信，在学术和临床研究组织之间公开坦诚地讨论数据质量保证问题，最终将在数据共享和再利用增加的未来时代使整个研究界受益。
