Understanding how selection bias impacts our ability to learn from health data
Article Title: A framework for understanding selection bias in real-world healthcare data Authors & Year: R. Kundu, X. Shi, J. Morrison, J. Barrett, and B. Mukherjee (2024)Journal: Journal of the Royal Statistical Society Series A: Statistics in Society [DOI:10.1093/jrsssa/qnae039]Review Prepared by Peter A. Gao Electronic health record (EHR) databases compile hundreds of thousands, or even millions, of patients’ medical histories, enabling researchers to study large populations and observe how their health evolves over time. The databases present an opportunity to identify risk factors for certain diseases, evaluate the efficacy of treatments for people of different backgrounds, and map health disparities. However, individuals are rarely included in such datasets at random, meaning the observed sample may not be representative of the target population. If certain groups are underrepresented in EHR data, using it to measure the prevalence of a condition or to assess the association between a risk factor and a…
Decoding Digital Preferences: A Collaborative Approach to Solving the Mysteries of A/B Testing
As a digital detective, your mission is to decipher the preferences of your website visitors. Your primary tool? A/B testing – a method used in online controlled experiments where two versions of a webpage (version A and version B) are presented to different subsets of users under the same conditions. It’s akin to a magnifying glass, enabling you to scrutinize the minute details of user interactions across two versions of a webpage to discern their preferences. However, this case isn’t as straightforward as it seems. A recent article by Nicholas Larsen et al. in The American Statistician reveals the hidden challenges of A/B testing that can affect the results of online experiments. If these challenges aren’t tackled correctly, they can lead to misleading conclusions, affecting decisions in both online businesses and academic research.