Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.7K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.7K
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

116
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
116
Range00:59

Range

13.5K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
13.5K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

307
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
307
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

78
It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
78
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

12.5K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Toward Evidence Synthesis of Adverse Events in Imbalanced Time-to-Event Data.

Journal of evidence-based medicine·2026
Same author

Gut microbial bile salt hydrolase as a metabolic gatekeeper in digestive homeostasis and disease.

Frontiers in immunology·2026
Same author

Trial design-aware funnel plot for publication bias assessment with noninferiority or equivalence objectives.

Journal of clinical epidemiology·2026
Same author

Ensitrelvir for the treatment of hospitalized adults with COVID-19: an international phase 3 randomized placebo-controlled trial.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Unpublished trials affected evidence synthesis substantially when estimating medication harms in children.

Journal of clinical epidemiology·2026
Same author

The hazards of using hazard ratios from proportional hazard models in indirect treatment comparisons.

Research synthesis methods·2026

Related Experiment Video

Updated: Dec 12, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

993

Estimating the reference range from a meta-analysis.

Lianne Siegel1, M Hassan Murad2, Haitao Chu1

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA.

Research Synthesis Methods
|August 14, 2020
PubMed
Summary
This summary is machine-generated.

Clinicians can now estimate normal reference ranges for new subjects using meta-analyses. Three novel methods account for healthy individual variations, improving upon pooled means in biomedical studies.

Keywords:
meta-analysisnormative dataprediction intervalrandom effects modelreference range

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.4K

Related Experiment Videos

Last Updated: Dec 12, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

993
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.4K

Area of Science:

  • Biostatistics
  • Clinical Reference Range Estimation
  • Meta-Analysis Applications

Background:

  • Clinicians require normal reference ranges to interpret individual patient measurements.
  • Existing meta-analyses of healthy populations often report only pooled means, ignoring inter-study variation.
  • There is a lack of guidance for estimating a new subject's reference range from existing meta-analyses.

Purpose of the Study:

  • To introduce three novel methods for calculating normal reference ranges from meta-analyses.
  • To provide guidance for estimating a new subject's reference range incorporating population variability.
  • To address the limitations of current meta-analytic approaches in reference range determination.

Main Methods:

  • Development of a frequentist random effects model for reference range calculation.
  • Implementation of a Bayesian random effects model for reference range estimation.
  • Application of an empirical approach for calculating normal reference ranges.

Main Results:

  • A simulation study demonstrated the good performance of the proposed methods across various scenarios.
  • The methods effectively incorporate natural variation among healthy individuals from different studies.
  • Caution is advised with small numbers of studies and high between-study heterogeneity.

Conclusions:

  • The presented methods offer a significant advancement in estimating normal reference ranges from meta-analyses.
  • These approaches provide a more comprehensive understanding of individual measurements within a healthy population.
  • Successful application to pediatric sleep and postural vertical measurements highlights practical utility.