Estimating the reference interval from a fixed effects <scp>meta‐analysis</scp>

Cao, Wenhao; Siegel, Lianne; Zhou, Jincheng; Zhu, Motao; Tong, Tiejun; Chen, Yong; Chu, Haitao

doi:10.1002/jrsm.1488

Cited by 3 publications

(19 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fixed-effects model does not assume a specific relationship between the study means, so it is particularly useful when the number of studies is relatively small (typically under 5). 9 To make the estimation of the reference range feasible, we further assume the population composed of all included studies represents the overall population. Although the mixture distribution method allows each study to have a distinct distribution (any distribution that is completely determined by its mean and variance), RIMeta makes the simplifying assumption that each study has a normal distribution with a different mean and variance.…”

Section: Mixture Distribution Methods Based On Fixed-effects Modelmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Cao et al 9 proposed a mixture distribution method for estimating the reference interval under the fixed‐effects model. The fixed‐effects model does not assume a specific relationship between the study means, so it is particularly useful when the number of studies is relatively small (typically under 5) 9 . To make the estimation of the reference range feasible, we further assume the population composed of all included studies represents the overall population.…”

Section: Methodsmentioning

confidence: 99%

“…31 to illustrate the use of RIMeta. Siegel et al 1 and Cao et al 9 also reanalyzed these data when proposing random effects methods and fixed-effects methods for estimating the reference interval from a meta-analysis.…”

Section: An Illustrative Examplementioning

confidence: 99%

“…7 Siegel et al 1,8 recently proposed four approaches for estimating the reference interval from a random effects meta-analysis using aggregate data: a frequentist approach, a Bayesian predictive approach, a Bayesian quantile approach, and an empirical approach. Cao et al 9 further proposed a mixture distribution method using a fixedeffects model. These methods have been implemented using R software, which not all researchers have the programming knowledge to use.…”

mentioning

confidence: 99%

See 4 more Smart Citations

RIMeta: An R shiny tool for estimating the reference interval from a meta‐analysis

Jiang

Cao

Chu

et al. 2023

Research Synthesis Methods

Self Cite

View full text Add to dashboard Cite

A reference interval, or an interval in which a prespecified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person's measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference interval estimated by combining the data across these studies is typically more generalizable than a reference interval based on a single study. Methods for estimating reference intervals from random effects meta‐analysis and fixed‐effects meta‐analysis have been recently proposed and implemented using R software. We present an R Shiny tool, RIMeta, implementing these methods, which allows users not proficient in R to estimate a reference interval from a meta‐analysis using aggregate data (mean, standard deviation, and sample size) from each study. RIMeta (https://cers.shinyapps.io/RIMeta/) provides users a convenient way to estimate a reference interval from a meta‐analysis and to generate the reference interval plot to visualize the results. The use of this web‐based R Shiny tool does not require the installation of R or any background knowledge of programming. We explain all functions of the R Shiny tool and illustrate how to use it with a real data example.

show abstract

Section: Mixture Distribution Methods Based On Fixed-effects Modelmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: An Illustrative Examplementioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

RIMeta: An R shiny tool for estimating the reference interval from a meta‐analysis

Jiang

Cao

Chu

et al. 2023

Research Synthesis Methods

Self Cite

View full text Add to dashboard Cite

show abstract

An improved Bayesian approach to estimating the reference interval from a meta‐analysis: Directly monitoring the marginal quantiles and characterizing their uncertainty

Siegel

Chu

2023

Research Synthesis Methods

Self Cite

View full text Add to dashboard Cite

Reference intervals, or reference ranges, aid medical decision‐making by containing a pre‐specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta‐analysis based on a random effects model: a frequentist approach, a Bayesian posterior predictive interval, and an empirical approach. Because the Bayesian posterior predictive interval becomes wider to incorporate estimation uncertainty, it may systematically contain greater than 95% of measurements when the number of studies is small or the between study heterogeneity is large. The frequentist and empirical approaches also captured a median of less than 95% of measurements in this setting, and 95% confidence or credible intervals for the reference interval limits were not developed. In this update, we describe how one can instead use Bayesian methods to summarize the appropriate quantiles (e.g., 2.5th and 97.5th) of the marginal distribution of individuals across studies and construct a credible interval describing the estimation uncertainty in the lower and upper limits of the reference interval. We demonstrate through simulations that this method performs well in capturing 95% of values from the marginal distribution and maintains a median coverage of near 95% of the marginal distribution even when the number of studies is small, or the between‐study heterogeneity is large. We also compare the results of this method to those obtained from the three previously proposed methods in the original case study of the meta‐analysis of frontal subjective postural vertical measurements.

show abstract

A Bayesian nonparametric meta‐analysis model for estimating the reference interval

Cao,

Chu,

Hanson

et al. 2024

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta‐analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study‐specific means and equal within‐study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta‐analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within‐study variances do not hold.

show abstract

Estimating the reference interval from a fixed effects meta‐analysis

Cited by 3 publications

References 40 publications

RIMeta: An R shiny tool for estimating the reference interval from a meta‐analysis

RIMeta: An R shiny tool for estimating the reference interval from a meta‐analysis

An improved Bayesian approach to estimating the reference interval from a meta‐analysis: Directly monitoring the marginal quantiles and characterizing their uncertainty

A Bayesian nonparametric meta‐analysis model for estimating the reference interval

Contact Info

Product

Resources

About