BACKGROUND The contribution of the statistician to the design and analysis of a clinical trial is acknowledged as essential. Ability to reconstruct the statistical contribution to a trial requires rigorous and transparent documentation as evidenced by the reproducibility of results. The process of validating statistical programmes is a key requirement. While guidance relating to software development and life cycle methodologies details steps for validation by information systems developers, there is no guidance applicable to programmes written by statisticians. We aimed to develop a risk-based approach to the validation of statistical programming that would support scientific integrity and efficient resource use within clinical trials units. METHODS The project was embedded within the Information Systems Operational Group and the Statistics Operational Group of the UK Clinical Research Collaboration Registered Clinical Trials Unit network. Members were asked to share materials relevant to validation of statistical programming. A review of the published literature, regulatory guidance and knowledge of relevant working groups was undertaken. Surveys targeting the Information Systems Operational Group and Statistics Operational Group were developed to determine current practices across the Registered Clinical Trials Unit network. A risk-based approach was drafted and used as a basis for a workshop with representation from statisticians, information systems developers and quality assurance managers (n = 15). The approach was subsequently modified and presented at a second, larger scale workshop (n = 47) to gain a wider perspective, with discussion of content and implications for delivery. The approach was revised based on the discussions and suggestions made. The workshop was attended by a member of the Medicines for Healthcare products Regulatory Agency Inspectorate who also provided comments on the revised draft. RESULTS Types of statistical programming were identified and categorised into six areas: generation of randomisation lists; programmes to explore/understand the data; data cleaning, including complex checks; derivations including data transformations; data monitoring; or interim and final analysis. The risk-based approach considers each category of statistical programme against the impact of an error and its likelihood, whether the programming can be fully prespecified, the need for repeated use and the need for reproducibility. Approaches to the validation of programming within each category are proposed. CONCLUSION We have developed a risk-based approach to the validation of statistical programming. It endeavours to facilitate the implementation of targeted quality assurance measures while making efficient use of limited resources.

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确定与风险相称的方法来验证临床试验的统计规划。

背景 统计学家对临床试验的设计和分析的贡献被认为是至关重要的。重建对试验的统计贡献的能力需要严格和透明的记录，结果的可重复性证明了这一点。验证统计程序的过程是一个关键要求。虽然与软件开发和生命周期方法论相关的指南详细说明了信息系统开发人员验证的步骤，但没有适用于统计学家编写的程序的指南。我们的目标是开发一种基于风险的方法来验证统计编程，以支持临床试验单位内的科学完整性和有效的资源利用。方法 该项目嵌入英国临床研究合作注册临床试验单位网络的信息系统运营组和统计运营组。要求成员分享与统计规划验证相关的材料。对已发表的文献、监管指南和相关工作组的知识进行了审查。针对信息系统运营小组和统计运营小组开展的调查是为了确定整个注册临床试验单位网络的当前做法。起草了基于风险的方法，并将其用作由统计学家、信息系统开发人员和质量保证经理 (n = 15) 代表参加的研讨会的基础。随后对该方法进行了修改，并在第二次更大规模的研讨会（n = 47）上进行了介绍，以获得更广泛的视角，并讨论了内容和对交付的影响。该方法根据讨论和提出的建议进行了修订。保健品药品监管机构监察员的一名成员参加了研讨会，他还对修订草案提出了意见。结果 统计规划的类型被确定并分为六个领域：生成随机列表；探索/理解数据的程序；数据清理，包括复杂的检查；包括数据转换在内的推导；数据监控；或中期和最终分析。基于风险的方法考虑了每一类统计程序的错误影响及其可能性、程序是否可以完全预先指定、重复使用的需要以及再现性的需要。提出了每个类别内编程验证的方法。结论 我们开发了一种基于风险的方法来验证统计规划。它努力促进有针对性的质量保证措施的实施，同时有效利用有限的资源。