This May, JMP Life Sciences is going on the road in Europe to demo some of the new features that will be available in the upcoming releases of JMP Clinical 4.1 and JMP Genomics 6.1. The Roadshow is an excellent opportunity to hear about new functionality, ask questions or perhaps sneak
We've gotten some good feedback on our new review features that will become available in the upcoming JMP Clinical 4.1. If you're new to the conversation, feel free to catch up here, here and here. The ability of JMP Clinical to identify new or modified data from snapshot to snapshot,
JMP Clinical 4.1 contains example data to help illustrate its new review functionality. This additional data is referred to as Nicardipine Early Snapshot, and includes Nicardipine data only through 01 Aug 1989. There are numerous changes to this data set: 11 subjects have yet to enroll in the trial, the
In last week’s post, we discussed some of the upcoming features of JMP Clinical 4.1 that identify new and modified records when clinical trial data is updated. These tools can greatly accelerate clinical reviews, allowing the clinician, statistician or data manager to focus exclusively on unreviewed records. Here we discuss
Over the next few posts, I discuss the data review process for clinical trials and highlight some new features for JMP Clinical 4.1 that streamline this monumental endeavor. Ideally, the data from a clinical trial should be examined by as many eyes as possible – including data and protocol managers,
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
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
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”
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
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