Factors such as sample size, power, effect sizes, and measures of variation might seem like tertiary considerations when investigators conceptualize rheumatology research, but these foundational design elements can sink studies before they begin.
STATS BOOTCAMP: Introduction to Statistical Power and Sample Size Calculation: What It Means and Why It Is Important will provide a practical overview of key statistical concepts essential for interpreting and designing rheumatology research, and explain how they influence study validity across common analyses used in rheumatology.
The session is the first of two STATS BOOTCAMP offerings at ACR Convergence 2026. It will take place on Monday, November 9, from 8:30–9:30 a.m., in Room W308 of the Orange County Convention Center. It will also outline the information needed to conduct sample size or power calculations and provide guidance for using clear, consistent language when reporting these elements in grant applications and scientific writing.
“Statistical power and sample size are critically important pieces of study design, because many times, the merit of research is judged on the basis of p-values and confidence intervals,” explained Todd Schwartz, DrPH, Professor and Associate Chair in the Department of Biostatistics at the University of North Carolina at Chapel Hill.
Dr. Schwartz will use visual methods and share mathematical formulas to illustrate key statistical concepts and show how these quantities are directly or inversely inter-related.
“We want to make sure we’re accurately determining the size of these studies, which means we want to ensure they’re big enough, but also that they’re not bigger than they need to be,” Dr. Schwartz said. “When studies are underpowered, we’re less likely to establish statistical significance, even if they’re finding clinically important effects. However, the bigger a study is, the more expensive and time-consuming it becomes for researchers as well as participants.”
Brooke McGinley, MS, a PhD candidate in biostatistics at the Boston University School of Public Health, will share practical examples of how these calculations are made and how to communicate them with others and in grants.
“A common misconception is that we can just plug a few numbers into a formula and get a sample size. However, that’s not all that’s entailed,” McGinley said. “We need to think about the study as a whole. We want to make sure that we’re achieving the sample size that can give us the evidence we’re interested in, as well as taking into account outside factors, such as feasibility, cost, and some of the ethical decisions that happen when you’re designing a study from beginning to end.”
One way researchers can set themselves up for statistical success is to define their hypothesis as clearly as possible and confirm it’s backed by scientific literature. McGinley also highlighted the importance of consulting a statistician.
“One of the biggest things is making sure that you’re meeting with a statistician as early as possible, when you’re thinking about designing a study, so they can be involved in the whole process, not just when you need a calculation completed,” she said.
STATS BOOTCAMP: Start Smart: How to Plan, Inspect, and Visualize Clinical Data will extend the series and will take place on Tuesday, November 10, from 3–4:30 p.m., also in Room W308. Participants will learn how to develop a statistical analysis plan, explore and visualize raw data to identify patterns and potential issues, and conduct essential initial data analysis before formal modeling.
Recordings of both sessions will be available to registered participants of ACR Convergence 2026 for on-demand viewing.
JOIN US IN ORLANDO
If you haven’t registered for ACR Convergence 2026, register today to participate in this year’s premier rheumatology experience, November 6–11 in Orlando, Florida. All registered participants receive on-demand access to scientific sessions after the meeting through September 30, 2027.
