Validating clinical trial data

There were other data cleaning activities, such as data management and medical review of data, which can generate queries resulting in data changes.” The additional tests consisted of looking at the three percent figure to determine how many of the changes were directly related to SDV. The study determined that only about 1.1 percent of all data changes were related to SDV.Once the team gathered information on data that was changed been entered into the EDC system and thus cannot be determined through SDV (if the data isn’t there, it can’t be cross-checked and verified).Given notes there are different types of data checks, and companies should acknowledge that they are not equal.“When you think about strategies on how to look at incorporating SDV and SDR in the future, there’s probably more of a bias towards looking for adverse event detection versus cleaning data that is already clean,” he says.The paper also solicited feedback from readers, with the hope that additional proof points would support the notion that SDV has a low value impact in overall clinical data quality.

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Information like this, at the very least, will give them a lot more comfort in getting this effort off the ground.In an industry that seems to be focused on cutting the cost of clinical trials, it’s no surprise that reducing the amount of source data verification (SDV) performed in studies—the process of cross-referencing data recorded in a case-report form to the original source information—is an integral part of risk-based monitoring (RBM) strategies.Eliminating source data checks that do not add value to the study is certainly a breakthrough for trials where we have historically performed 100 percent data verification.“There are multiple sources of data, such as lab science and clinical science systems, which can create an adverse event.We were hoping to determine the percentage of adverse events entered into an EDC system after SDR occurred.

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