Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
Clin Trials. 2013 Apr;10(2):225-35. doi: 10.1177/1740774512469312. Epub 2013 Jan 2.
Central statistical monitoring in multicenter trials could allow trialists to identify centers with problematic data or conduct and intervene while the trial is still ongoing. Currently, there are few published models that can be used for this purpose.
To develop and validate a series of risk scores to identify fabricated data within a multicenter trial, to be used in central statistical monitoring.
We used a database from a multicenter trial in which data from 9 of 109 centers were documented to be fabricated. These data were used to build a series of risk scores to predict fraud at centers. All analyses were performed at the level of the center. Exploratory factor analysis was used to select from 52 possible predictors, chosen from a variety of previously published methods. The final models were selected from a total of 18 independent predictors, based on the factors identified. These models were converted to risk scores for each center.
Five different risk scores were identified, and each had the ability to discriminate well between centers with and without fabricated data (area under the curve values ranged from 0.90 to 0.95). True- and false-positive rates are presented for each risk score to arrive at a recommended cutoff of seven or above (high risk score). We validated these risk scores, using an independent multicenter trial database that contained no data fabrication and found the occurrence of false-positive high scores to be low and comparable to the model-building data set.
These risk score have been validated only for their false-positive rate and require validation within another trial that contains centers that have fabricated data. Validation in noncardiovascular trials is also required to gage the usefulness of these risk scores in central statistical monitoring.
With further validation, these risk scores could become part of a series of tools that provide evidence-based central statistical monitoring, which in turn can improve the efficiency of trials, and minimize the need for more expensive on-site monitoring.
在多中心试验中进行中心统计监测，可以让试验人员识别出数据存在问题的中心，并在试验仍在进行时进行监测和干预。目前，很少有可用于此目的的已发表模型。
开发和验证一系列风险评分，以识别多中心试验中的伪造数据，用于中心统计监测。
我们使用了一项多中心试验的数据库，其中 109 个中心中的 9 个中心的数据被记录为伪造数据。这些数据用于构建一系列预测中心欺诈的风险评分。所有分析均在中心层面进行。探索性因子分析用于从之前发表的各种方法中选择的 52 个可能的预测因子中进行选择。根据确定的因素，从总共 18 个独立预测因子中选择最终模型。这些模型被转换为每个中心的风险评分。
确定了五个不同的风险评分，每个评分都能够很好地区分有伪造数据和无伪造数据的中心（曲线下面积值范围从 0.90 到 0.95）。为每个风险评分呈现了真阳性和假阳性率，以确定推荐的 7 分或以上（高风险评分）的截断值。我们使用包含无数据伪造的另一个多中心试验数据库验证了这些风险评分，发现高风险评分的假阳性发生率较低，与模型构建数据集相当。
这些风险评分仅在其假阳性率方面得到验证，需要在包含伪造数据的另一个试验中进行验证。还需要在非心血管试验中进行验证，以评估这些风险评分在中心统计监测中的有效性。
经过进一步验证，这些风险评分可以成为一系列提供基于证据的中心统计监测工具的一部分，这反过来又可以提高试验的效率，并最大限度地减少对更昂贵的现场监测的需求。
