Application of methods for central statistical monitoring in clinical trials
Application of methods for central statistical monitoring in clinical trials
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DOI：
10.1177/1740774513494504
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发表时间：
2013-10-01
期刊：
影响因子：
2.7
通讯作者：
Hackshaw, Allan
中科院分区：
文献类型：
--
作者：
Kirkwood, Amy A.;Cox, Trevor;Hackshaw, Allan
背景 现场源数据验证是一项常见且昂贵的活动，几乎没有证据表明这是值得的。中央统计监控 (CSM) 是一种更便宜的替代方案，其中数据检查由协调中心执行，无需访问所有站点。一些出版物提出了 CSM 的方法；然而，很少有人描述它们在实际试验中的用途。 方法 R 程序的创建是为了使用先前描述的方法或我们开发的新方法在受试者级别（3 个程序中的 7 个测试）或站点级别（8 个程序中的 9 个测试）检查数据。这些旨在发现可能的数据错误，例如异常值、不正确的日期或异常的数据模式；数字偏好、数值与均值太接近或太远、不寻常的相关结构、可能表明欺诈或程序错误以及不良事件少报的极端差异。这些方法应用于三项试验，其中一项试验已经结束并已发表，一项正在进行后续试验，第三项试验添加了伪造的数据。我们检查了这些方法的效果如何，讨论了它们的优点和局限性。结果 R 程序生成了简单的表格或易于阅读的图形。前两次试验中几乎没有发现数据错误，而第三次试验中添加的数据错误很容易被发现。该程序能够根据单个或多个变量识别异常值患者。他们还检测到（1）伪造的患者，生成的值太接近多变量平均值，或者重复测量的方差太低，以及（2）具有异常相关结构或不良事件太少的站点。如果应用于患者较少的中心，或者如果数据的制作方式不符合创建程序所用的假设，则某些方法是不可靠的。 R 程序的输出使用示例进行解释。 局限性 检测数据错误相对简单；然而，在检测欺诈方面存在一些局限性：某些程序无法应用于小型试验或患者较少的中心（
Background On-site source data verification is a common and expensive activity, with little evidence that it is worthwhile. Central statistical monitoring (CSM) is a cheaper alternative, where data checks are performed by the coordinating centre, avoiding the need to visit all sites. Several publications have suggested methods for CSM; however, few have described their use in real trials.Methods R-programs were created to check data at either the subject level (7 tests within 3 programs) or site level (9 tests within 8 programs) using previously described methods or new ones we developed. These aimed to find possible data errors such as outliers, incorrect dates, or anomalous data patterns; digit preference, values too close or too far from the means, unusual correlation structures, extreme variances which may indicate fraud or procedural errors and under-reporting of adverse events. The methods were applied to three trials, one of which had closed and has been published, one in follow-up, and a third to which fabricated data were added. We examined how well the methods work, discussing their strengths and limitations.Results The R-programs produced simple tables or easy-to-read figures. Few data errors were found in the first two trials, and those added to the third were easily detected. The programs were able to identify patients with outliers based on single or multiple variables. They also detected (1) fabricated patients, generated to have values too close to the multivariate mean, or with too low variances in repeated measurements, and (2) sites which had unusual correlation structures or too few adverse events. Some methods were unreliable if applied to centres with few patients or if data were fabricated in a way which did not fit the assumptions used to create the programs. Outputs from the R-programs are interpreted using examples.Limitations Detecting data errors is relatively straightforward; however, there are several limitations in the detection of fraud: some programs cannot be applied to small trials or to centres with few patients (