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”
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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
A forest plot (Figure 1) is a convenient way to graphically display several confidence or credible intervals and is often used in meta-analysis. Here, the x-axis represents the treatment effect between two interventions while the y-axis refers to the individual studies from which the intervals are obtained, or to various consensus intervals derived
With more than 6,000 attendees, the Joint Statistical Meetings (JSM) is the largest gathering of statisticians held in North America. This year, the event takes place from July 28 to Aug. 2 in San Diego, California. Developers from JMP Life Sciences will be on hand at the JMP booth in
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
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
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
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
The analysis of adverse events (AEs) suffers from the problem of dimensionality. It is impossible to predict what AEs will occur on study, and there are often numerous events by study’s end. Typically, the incidence of adverse events is summarized in tables, with events coded by a medical dictionary, such