Richard Zink
Principal Research Statistician Developer

Richard C. Zink is Principal Research Statistician Developer in the JMP Life Sciences division at SAS Institute. He joined SAS in 2011 after eight years in the pharmaceutical industry, where he designed and analyzed clinical trials in multiple therapeutic areas and participated in US and European drug submissions and FDA advisory committee hearings. Richard is the Statistics Section Editor for Therapeutic Innovation & Regulatory Science (formerly Drug Information Journal), and holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill. Follow him at @rczink.

Sitting down with Nate Silver

 “Visually, it’s easier to present uncertainty.” – Nate Silver About a year ago, I approached the chair-elect of the Biopharmaceutical Section of the American Statistical Association and asked "How can I be involved?" He replied that there was a podcasting initiative they were hoping to get off the ground, and

Risk-based monitoring: Defining thresholds for risk

Now that we have put together a data set containing the important metrics to monitor safety and site performance, we need to define the thresholds that constitute elevated risk. Unfortunately, there is no one-size-fits all solution to this problem. The study population (e.g., pediatric, elderly or at particular risk of safety

Celebrating statisticians: William Sealy Gosset (a.k.a. Student)

To many of us, whether statistician or not, the name William Sealy Gosset may be unrecognizable. His pseudonym Student, however, reveals him as one of the most prominent statisticians in history. Student’s t-test is an important part of every introductory statistics course, making everyone from single-statistics-course students to those who

Risk-based monitoring: Let's start with the data

To begin our discussions on risk-based monitoring (RBM), we first need to start with the data. The data include various metrics to assess site performance, and may include several key measures of safety such as deaths and adverse events (to assess safety concerns or under-reporting). But where to start? These

Understanding shift plots in JMP Clinical

JMP Clinical has several features to summarize records from SDTM Findings domains, data that result from “planned evaluations to address specific tests or questions.” In other words, this includes any data from the myriad of tests or procedures that are performed as part of the doctor’s examination: laboratory tests, ECG

Statisticians: harbingers of doom?

Ladies and gentlemen, it’s official. We’ve reached the midpoint of the International Year of Statistics. Or as Bon Jovi would put it: “Whoa… we’re halfway there… whoa--OH!  livin’ on a prayer.” (Seriously, it’s hard not to sing this song when you reach the halfway point of anything.) If you somehow

Omne trium perfectum: JMP add-in for WinBUGS

There's a bit of Latin that states "omne trium perfectum" or "everything that comes in threes is perfect." I had not set out to write three posts in a row on Markov Chain Monte Carlo (MCMC), but sometimes the stars align in such a way that the story continues to

Analyzing adverse events using Bayesian hierarchical models

You may be asking yourself… “Two Bayesian posts in a row? What is going on?” Though my statistical training focused on Frequentist methodologies, I am a big believer in using whatever tools help me gain insight into the statistical problem I happen to be focusing on at the moment. Frequentist

Using JMP to evaluate MCMC diagnostics

It’s no secret that JMP excels in the visual exploration of data. There’s a healthy dose of statistics, too. But when asked about Bayesian methods, JMP is probably not the first software package that comes to mind. JMP 10 does contain Bayesian D-optimal and I-optimal designs in our design of experiments (DOE) features,