7 things to love about JMP Clinical 5.0

New versions of JMP Clinical and Genomics are available starting today, so I wanted to take the opportunity to give a brief overview of some of the new features you’ll come to enjoy with the new release of JMP Clinical 5.0. Below are seven things to love! 1. Risk-Based Monitoring […]

Assessing the similarity of clinical trial subjects within study site

We’ve reached the end of our series of posts on fraud detection in clinical trials (for now, at least). Our final discussion focuses on the similarity of subjects within the clinical site, a topic that I hinted at in my response to a comment to one of my earlier posts. As part […]

Identifying multivariate inliers and outliers

We’re nearing the end of this series of posts on fraud detection in clinical trials and some upcoming features of JMP Clinical 4.1 that help identify unusual observations. We’ve described how visit dates and measurements taken in the clinic can signify problems at the clinical site, and discussed how trial […]

Clinical trial visit dates can signal something is amiss

Sometimes we’re so wrapped up in the day-to-day activities of conducting a clinical trial that we forget to perform some of the more obvious data checks. Data cleaning activities that seem unlikely to bear fruit because “I can’t believe they would do this” or “no way they could mess this up” […]

Locating potentially fraudulent data in SDTM Findings domains

We continue our discussion of unusual and potentially fraudulent data from clinical trials, and today we focus on data that occur in CDISC Findings domains. From the SDTM Implementation Guide, Findings domains represent data from planned evaluations that address specific tests or questions. For example, these domains contain data from […]

Identifying re-enrolled subjects in clinical trials

Over the next several blog posts, I will discuss various analyses that can be used to identify unusual and potentially fraudulent data from clinical trials. See Buyse at al. for a good summary of clinical trial fraud, including suggestions for statistical methods to help identify unusual data. JMP Clinical 4.1 […]