Related Experiment Video
Updated: Sep 10, 2025
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
A Parametric Empirical Bayes Approach to Personalized Reference Intervals and Reference Change Values
Eirik Åsen Røys1,2, Kristin Viste1,2, Christopher-John Farrell3
1Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.
Background:
Population-wide reference intervals (RIpop) are commonly used in laboratory medicine but may not reflect an individual's tightly regulated homeostatic interval. Personalized reference intervals (RIper) could enhance diagnostic precision by accounting for individual variability. A parametric empirical Bayes (PEB) framework stabilizes individual estimates using population parameters, enabling reliable RIper even from a limited number of individual results.
Methods:
We applied the PEB framework to estimate RIper for 9 biomarkers: albumin, creatinine, phosphate, cortisone, cortisol, testosterone, androstenedione, 17-hydroxyprogesterone, and 11-deoxycortisol. The PEB parameters tested were derived from both routine Laboratory Information System (LIS) data and a local biological variation (BV) study. Using serial samples from healthy adults, we assessed the proportion of results flagged with a 95% prediction interval and compared RIper to conventional RIpop and reference change values (RCVs).
Results:
LIS parameters were based on data from 1986 to 185 488 patients. PEB-based RIper were consistently narrower than RIpop while maintaining or reducing the proportion of flagged results. For example, albumin flagging decreased from 4.7% (RIpop) to 0.3% (RIper), phosphate from 5.4% to 3.7%, and cortisone from 7.1% to 3.9%. Conversely, 17-hydroxyprogesterone increased from 0.0% to 5.5% but remained close to the expected 5%. PEB thresholds were narrower than standard RCV estimates by correcting for regression toward the mean without increasing flagged results.
Conclusions:
The PEB framework effectively provides personalized cutoffs for laboratory tests even when few individual patient results are available. PEB parameters can be established using LIS or BV data, offering a feasible and cost-effective implementation pathway.
More Related Videos
07:28Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
Published on: January 21, 2017
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
01:21Confidence Intervals
A...
01:26Distributions to Estimate Population Parameter
01:24Kaplan-Meier Approach
01:14Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...