Each week, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Statistics in Medicine, with the full article now published in Volume 40, Issue 4 and available to read here.

Wang, M‐C, Yang, Y. Complexity and bias in cross‐sectional data with binary disease outcome in observational studies. Statistics in Medicine. 2021; 40: 950– 962. https://doi.org/10.1002/sim.8812

A cross sectional population is defined as a population of living individuals at the sampling or observational time. Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies for understanding how risk factors correlate with disease outcome. This paper presents the complexity and bias in cross-sectional data with binary disease outcome. We argue that the distribution of cross-sectional binary outcome is very different from risk distribution from the target population and that bias is typically present in standard data analysis such as logistic regression analysis. Through explicit formulas we conclude that bias can almost never be avoided from cross-sectional data. We present age-specific risk probability (ARP) and argue that models based on ARP offers a compromised but still biased approach to understand the population risk. An analysis based on Alzheimer’s disease data is presented to illustrate the ARP model and possible critiques from the analysis results.