This past week, Nate Silver held an “Ask Me Anything” chat on Reddit. There were several very good questions, one of which I found particularly important as we begin the International Year of Statistics: “What is the biggest abuse of statistics”? To which Nate replied: “Overfitting.” This response is very
You may have heard the news that 2013 is the International Year of Statistics, a wordwide celebration of the contributions of statistics, and it couldn’t have come at a better time. Nate Silver’s near-perfect prediction of the presidential election and popular fare such as the recent Oscar-nominated Brad Pitt-starring film Moneyball
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
Congratulations to Russ Wolfinger, PhD, who was elected a 2012 Fellow of the American Association for the Advancement of Science! Wolfinger was chosen for his "path-breaking statistical software used to analyze correlated data, promotion of statistical reasoning in science, and leadership in analysis of gene expression data," the AAAS said.
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
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
People are living longer -- but why is this, and how can we remain healthy late in life? That was the focus of a recent CBS Sunday Morning story, which also showed JMP software in action in a research lab. Researchers have been studying some centenarians to figure out their