PDF(Pubmed)
Abstract：
BACKGROUND: The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use.
METHODS: A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions).
RESULTS: Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing (\"assessor\") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as \"gold standard\" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated.
CONCLUSIONS: The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS.
BACKGROUND: The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).
METHODS: A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions).
RESULTS: Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing (\"assessor\") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as \"gold standard\" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated.
CONCLUSIONS: The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS.
BACKGROUND: The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).
摘要：
背景：为临床试验提供数据共享声明（DSS）已被不同的利益相关者强制要求。DSS是用于澄清是否存在共享个体参与者数据(IPD)的意图的设备。缺少的是对DSS是否提供有关IPD二次使用数据共享条件的清晰易懂的信息的详细评估。
方法：从ECRIN临床研究元数据储存库中随机抽取200个带有明确DSS的COVID-19临床试验。对DSS进行了评估和分类，由两名经验丰富的专家和一名在数据共享(DS)方面经验较少的评估员，分为不同的类别(不清楚，没有分享,没有计划,是的，但含糊不清，是的，根据要求，是指定的存储位置，是的，但条件复杂)。
结果：两位专家之间的一致是中等到实质性的（kappa=0.62,95%CI[0.55,0.70]）。当这些专家与经验不足且缺乏数据共享培训的第三人（“评估员”）（kappa=0.33,95%CI[0.25,0.41]；0.35,95%CI[0.27,0.43]）进行比较时，一致性大大降低。在两位专家之间，在独立主持人的监督下，这些案件达成了共识，两位专家都不同意，结果被用作进一步分析的“黄金标准”。在63.5%(127/200)的病例中至少表达了一定程度的DS(数据共享)意愿。在这些案件中,大约一个季度(31/127)是模糊的支持数据共享的声明，但没有有用的细节。在大约一半的情况下(60/127)，有人说IPD可以通过请求获得。仅在略高于10％的情况下（15/127），有人指出IPD将被转移到特定的数据存储库。在其余情况下（21/127），描述或引用了一个更复杂的制度，无法分配给前三个组中的一个。由于协商一致的会议，分类系统已更新。
结论：研究表明，当前的DSS暗示可能的数据共享通常不容易解释，即使是相对有经验的员工。基于机器的解释，这对于任何实际应用都是必要的，目前是不可能的。机器学习和/或自然语言处理技术可能会提高机器的可操作性，但将代表一个非常大的投资的研究努力。对于数据提供商来说，更便宜、更容易的选择是，数据请求者，资助者和平台采用更清晰的，更结构化、更标准化的指定方法，提供和收集DSS。
背景：该研究的协议已在ZENODO上预先注册（https://zenodo.org/record/7064624＃。Y4DIAHbMJD8）。
方法：从ECRIN临床研究元数据储存库中随机抽取200个带有明确DSS的COVID-19临床试验。对DSS进行了评估和分类，由两名经验丰富的专家和一名在数据共享(DS)方面经验较少的评估员，分为不同的类别(不清楚，没有分享,没有计划,是的，但含糊不清，是的，根据要求，是指定的存储位置，是的，但条件复杂)。
结果：两位专家之间的一致是中等到实质性的（kappa=0.62,95%CI[0.55,0.70]）。当这些专家与经验不足且缺乏数据共享培训的第三人（“评估员”）（kappa=0.33,95%CI[0.25,0.41]；0.35,95%CI[0.27,0.43]）进行比较时，一致性大大降低。在两位专家之间，在独立主持人的监督下，这些案件达成了共识，两位专家都不同意，结果被用作进一步分析的“黄金标准”。在63.5%(127/200)的病例中至少表达了一定程度的DS(数据共享)意愿。在这些案件中,大约一个季度(31/127)是模糊的支持数据共享的声明，但没有有用的细节。在大约一半的情况下(60/127)，有人说IPD可以通过请求获得。仅在略高于10％的情况下（15/127），有人指出IPD将被转移到特定的数据存储库。在其余情况下（21/127），描述或引用了一个更复杂的制度，无法分配给前三个组中的一个。由于协商一致的会议，分类系统已更新。
结论：研究表明，当前的DSS暗示可能的数据共享通常不容易解释，即使是相对有经验的员工。基于机器的解释，这对于任何实际应用都是必要的，目前是不可能的。机器学习和/或自然语言处理技术可能会提高机器的可操作性，但将代表一个非常大的投资的研究努力。对于数据提供商来说，更便宜、更容易的选择是，数据请求者，资助者和平台采用更清晰的，更结构化、更标准化的指定方法，提供和收集DSS。
背景：该研究的协议已在ZENODO上预先注册（https://zenodo.org/record/7064624＃。Y4DIAHbMJD8）。
