Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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.

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An improved approach to modelling patient reclassification: HbA<sub>1c</sub> as an example.

Clinical chemistry and laboratory medicine·2026

Same author

Is statistical evaluation sufficient? New external quality assessment performance metrics through clinical performance.

Clinical chemistry and laboratory medicine·2026

Same author

Investigating the gut microbiota in advanced heart failure and cardiac cachexia.

Gut microbes reports·2026

Same author

Voriconazole therapy and CYP2C19 phenotype: identifying patients who may need alternative antifungal therapy.

The Journal of antimicrobial chemotherapy·2026

Same author

Exploring Sex-Specific High-Sensitivity Cardiac Troponin Thresholds for Rule-Out of Non-ST-Elevation Myocardial Infarction.

Clinical chemistry·2026

Same author

Association between thyroidea function and opioid agonist therapy: a prospective cohort study from Norway.

Journal of the Endocrine Society·2026

Same journal

Comparison of Information-Dependent Acquisition and Sequential Window Acquisition of All Theoretical Mass Spectra for Untargeted Drug Testing on a Linear Ion Trap-Pulsing Quadrupole-Time of Flight Mass Spectrometer.

Clinical chemistry·2026

Same journal

Patterns of One-Year Change in HbA1c and Continuous Glucose Monitoring (CGM) Metrics in Older Adults with Type 2 Diabetes.

Clinical chemistry·2026

Same journal

TSH Pediatric Reference Intervals: Lack of CALIPER Applicability to US-Based Populations.

Clinical chemistry·2026

Same journal

Rapid Detection of Hemoglobinopathy Variants Using One-Step Library Preparation and Nanopore Sequencing.

Clinical chemistry·2026

Same journal

Editor's Note: Circulating Proteolytic Products of Carboxypeptidase N for Early Detection of Breast Cancer.

Clinical chemistry·2026

Same journal

In Reply to Reflexing NT-proBNP for sFlt-1/PlGF Ratios That Fall into the Measurement Uncertainty for Preeclampsia Risk Classification.

Clinical chemistry·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 10, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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 Røys^1,2, Kristin Viste^1,2, Christopher-John Farrell³

¹Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.

Clinical Chemistry

|August 22, 2025

Summary

This summary is machine-generated.

Personalized reference intervals (RIper) improve diagnostic precision by accounting for individual variability. The parametric empirical Bayes (PEB) framework enables reliable RIper using population data, even with limited individual results.

More Related Videos

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: Sep 10, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Laboratory medicine
Biomarker analysis
Personalized diagnostics

Background:

Population-wide reference intervals (RIpop) may not reflect individual homeostatic ranges.
Personalized reference intervals (RIper) can enhance diagnostic precision.
A parametric empirical Bayes (PEB) framework stabilizes individual estimates for reliable RIper.

Purpose of the Study:

To apply the PEB framework for estimating RIper for nine key biomarkers.
To compare PEB-based RIper with conventional RIpop and reference change values (RCVs).
To assess the feasibility of using routine Laboratory Information System (LIS) or biological variation (BV) data for PEB parameter establishment.

Main Methods:

Applied the PEB framework to estimate RIper for albumin, creatinine, phosphate, cortisone, cortisol, testosterone, androstenedione, 17-hydroxyprogesterone, and 11-deoxycortisol.
Derived PEB parameters from LIS data and a local BV study.
Assessed flagged results and compared RIper to RIpop and RCVs using serial samples from healthy adults.

Main Results:

PEB-based RIper were consistently narrower than RIpop, reducing flagged results for albumin, phosphate, and cortisone.
Flagging for 17-hydroxyprogesterone increased but remained close to the expected 5%.
PEB thresholds corrected for regression toward the mean, proving narrower than standard RCV estimates without increasing flagged results.

Conclusions:

The PEB framework effectively generates personalized cutoffs for laboratory tests, even with limited individual data.
PEB parameters can be derived from LIS or BV data, indicating a feasible implementation pathway.
This approach offers a cost-effective method for enhancing diagnostic precision through personalized reference intervals.