The Big Data Paradox in Clinical Practice

作者：Pavlos Msaouel · 发表于：Cancer Investigation · 年份：2022 · DOI：10.1080/07357907.2022.2084621 · 被引用次数：40 · 研究领域：Machine Learning in Healthcare、Statistical Methods and Inference、Colorectal Cancer Screening and Detection

The big data paradox is a real-world phenomenon whereby as the number of patients enrolled in a study increases, the probability that the confidence intervals from that study will include the truth decreases. This occurs in both observational and experimental studies, including randomized clinical trials, and should always be considered when clinicians are interpreting research data. Furthermore, as data quantity continues to increase in today's era of big data, the paradox is becoming more pernicious. Herein, I consider three mechanisms that underlie this paradox, as well as three potential strategies to mitigate it: (1) improving data quality; (2) anticipating and modeling patient heterogeneity; (3) including the systematic error, not just the variance, in the estimation of error intervals.