Editorial Commentary: Sometimes You Don’t Know What You’ve Got Until It’s Gone—The Effect of Missing Data in “Big Data” Studies

Abstract

Big-data studies are powerful tools for comparative-effectiveness research, but because of the large number of included patients, they risk falsely identifying a difference when none exists because large sample sizes may result in statistically significant differences that have little clinical importance. Other limitations of big-data studies include lack of generalizability because of inclusion of only specific patient populations, lack of validated outcome measures, recording bias or clerical error, and vast troves of missing data. As such, the methods and results of big-data studies require careful scrutiny to ensure that the conclusions are correct.