Estimating the reference interval from a fixed effects meta-analysis

A reference interval provides a basis for physicians to determine whether a measurement is typical of a healthy individual. It can be interpreted as a prediction interval for a new individual from the overall population. However, a reference interval based on a single study may not be representative...

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Bibliographic Details
Published in:Research synthesis methods Vol. 12; no. 5; p. 630
Main Authors: Cao, Wenhao, Siegel, Lianne, Zhou, Jincheng, Zhu, Motao, Tong, Tiejun, Chen, Yong, Chu, Haitao
Format: Journal Article
Language:English
Published: England 01.09.2021
Subjects:
Computer Simulation
Female
Humans
Models, Statistical
Normal Distribution
Reference Values
Research Design
meta-analysis
very few studies
fixed effects model
reference interval
ISSN:1759-2887, 1759-2887
Online Access:Get more information
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Summary:A reference interval provides a basis for physicians to determine whether a measurement is typical of a healthy individual. It can be interpreted as a prediction interval for a new individual from the overall population. However, a reference interval based on a single study may not be representative of the broader population. Meta-analysis can provide a general reference interval based on the overall population by combining results from multiple studies. Methods for estimating the reference interval from a random effects meta-analysis have been recently proposed to incorporate the within and between-study variation, but a random effects model may give imprecise estimates of the between-study variation with only few studies. In addition, the normal distribution of underlying study-specific means, and equal within-study variance assumption in these methods may be inappropriate in some settings. In this article, we aim to estimate the reference interval based on the fixed effects model assuming study effects are unrelated, which is useful for a meta-analysis with only a few studies (e.g., ≤5). We propose a mixture distribution method only assuming parametric distributions (e.g., normal) for individuals within each study and integrating them to form the overall population distribution. This method is compared to an empirical method only assuming a parametric overall population distribution. Simulation studies have shown that both methods can estimate a reference interval with coverage close to the targeted value (i.e., 95%). Meta-analyses of women daytime urination frequency and frontal subjective postural vertical measurements are reanalyzed to demonstrate the application of our methods.
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ISSN:1759-2887
1759-2887
DOI:10.1002/jrsm.1488