Tag: Clinical

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

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

Disproportionality analysis is coming in JMP Clinical 4.0

While randomized clinical trials are the gold standard for evaluating the efficacy of a new intervention, the available sample size is often insufficient to fully understand its safety profile. The risk a new therapy may pose may not be well understood until it has been on the market for many

JMP Clinical at CDISC European Interchange

Every year, the Clinical Data Interchange Standards Consortium (CDISC) holds several Interchange events that take place in the US, Europe and Asia. These meetings have several goals: to give CDISC users the opportunity to present solutions to data challenges that arise in the day-to-day use of these standards; to provide new or

JMP Genomics shows the future of biomarker discovery

The Biomarkers Congress is Europe’s largest biomarkers event showcasing case studies in biomarker discovery, and validation strategies and regulation. Its industry delegate list reads like a who’s who in the world of biomarkers, with representation from throughout the globe, for example, AstraZeneca, GSK, Merck, Roche and Takeda. It was fantastic

JMP Clinical present at PhUSE, Brighton 2011

The Pharmaceutical Users Software Exchange (PhUSE) conference in the British seaside resort of Brighton was nearly two months ago, but I am still thinking about it; PhUSE provided an opportunity to learn from experts and share ideas about the application of software in the pharmaceutical industry. The theme for this

Accounting for the time at which an adverse event occurs

In a previous post, I described how JMP Clinical allows you to specify time windows within an incidence analysis. Specifying time windows can provide a more informative analysis since it is possible to view how the risk of adverse events (AEs) changes over the course of a clinical trial. However, the

Discovering unreported adverse events using your findings data

When designing case report forms (CRFs) for a clinical trial, it is important to minimize or eliminate redundancies in the collected information. Such redundancies can lead to inconsistencies that require a query to the clinical site for resolution. In a poorly designed CRF, data conflicts can be so numerous that

JMP Genomics release features new analytic tools

JMP Genomics 5.1 has been released! Although it feels like we’ve been working on this release for a long time, it was only last November when JMP Genomics 5.0 went out the door. What have we been up to during that time? Quite a lot! One big change for our