Clinicians frequently must decide whether a patient's measurement reflects that of a healthy "normal"individual. Thus, the reference range is defined as the interval in which some proportion (frequently 95%) of measurements from a healthy population is expected to fall. One can estimate it from a single study or preferably from a meta-analysis of multiple studies to increase generalizability. This range differs from the confidence interval for the pooled mean and the prediction interval for a new study mean in a meta-analysis, which do not capture natural variation across healthy individuals. Methods for estimating the reference range from a meta-analysis of aggregate data that incorporates both within- and between-study variations were recently proposed. In this guide, we present 3 approaches for estimating the reference range: one frequentist, one Bayesian, and one empirical. Each method can be applied to either aggregate or individual-participant data meta-analysis, with the latter being the gold standard when available. We illustrate the application of these approaches to data from a previously published individual-participant data meta-analysis of studies measuring liver stiffness by transient elastography in healthy individuals between 2006 and 2016.
Bibliographical noteFunding Information:
This work was funded by the National Heart, Lung, and Blood Institute (grant T32HL129956) and the National Library of Medicine (grant R01LM012982)
© 2022 The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.
- normative data
- prediction interval
- random effects
- reference range
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural