The lay abstract featured today (for Dynamic Treatment Effect Analysis in Crossover Designs Through Repeated Measures by Jianping Sun, Peiran Guo, Xiaoyang Chen and Xianming Tan) is from Statistics in Medicine with the full article now available to read here.

How to cite

Sun, J., Guo, P., Chen, X. and Tan, X. (2025), Dynamic Treatment Effect Analysis in Crossover Designs Through Repeated Measures. Statistics in Medicine, 44: e70070. https://doi.org/10.1002/sim.70070

Lay Abstract for “Dynamic Treatment Effect Analysis in Crossover Designs through Repeated Measures” – A Fresh Look at How Treatments Work Over Time

Clinical trials often use crossover designs, where participants receive different treatments in sequence. This approach can be efficient, but it comes with a challenge: how do we separate the lingering effects of the first treatment (known as “carryover”) from the effects of the second treatment?

This study introduces a novel statistical approach to this problem. Instead of treating medication effects as static, the new approach sees them as dynamic curves that change over time—much like how pain relief from a medication gradually increases, peaks, and then fades away.

By analyzing multiple measurements taken at different time points, this method creates a complete picture of how each treatment affects patients over time. This eliminates the need to make assumptions about carryover effects, which have traditionally been a major headache for researchers.

This approach is particularly valuable for pharmaceutical studies, where understanding how drugs work over time is crucial. It works for various treatment scenarios—whether a drug is given once, multiple times, or at different doses.

The method has shown promising results in both simulated studies and real-world clinical data. By capturing the full timeline of treatment effects, researchers can design more efficient trials and gain deeper insights into how treatments truly compare.

This research marks an important step forward in clinical trial methodology, potentially leading to more accurate drug comparisons and ultimately better treatment decisions for patients.