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

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.7K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.7K
Data Validation01:15

Data Validation

184
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
184
Reliability and Validity01:29

Reliability and Validity

12.8K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Statistical Significance01:50

Statistical Significance

20.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
20.2K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

412
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
412

You might also read

Related Articles

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

Sort by
Same author

Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology.

Nature communications·2026
Same author

GEiPRS: a fast and powerful machine learning method for polygenic risk score prediction by leveraging genotype-environment interactions.

Briefings in bioinformatics·2026
Same author

PERADIGM: Phenotype embedding similarity-based rare disease gene mapping.

PLoS genetics·2025
Same author

Recessive genomic and phenotypic variation in consanguineous families with cerebral palsy.

medRxiv : the preprint server for health sciences·2025
Same author

Assessing Benefit in Patients With Heart Failure and Reduced Ejection Fraction: Analysis of the VICTORIA Trial Using Novel Prognostic Risk Stratification.

Journal of cardiac failure·2025
Same author

Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions.

Nature genetics·2025
Same journal

Modeling treatment effects on absorbing outcomes in clinical trials: Leveraging longitudinal and ordinal data for efficiency gains.

Statistical methods in medical research·2026
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Jul 20, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Statistical assessment of biomarker replicability using MAJAR method.

Yuhan Xie1, Song Zhai2, Wei Jiang1

  • 1Department of Biostatistics, Yale University, New Haven, CT, USA.

Statistical Methods in Medical Research
|July 31, 2023
PubMed
Summary
This summary is machine-generated.

A new framework, MAJAR, enhances biomarker discovery by jointly testing effects and assessing replicability. This improves statistical power and controls false discovery rates for precision medicine applications.

Keywords:
Joint effectmeta-analysispharmacogenomics GWASreplicability assessment

More Related Videos

A Cost Effective and Adaptable Scratch Migration Assay
08:59

A Cost Effective and Adaptable Scratch Migration Assay

Published on: June 30, 2020

5.5K
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.3K

Related Experiment Videos

Last Updated: Jul 20, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
A Cost Effective and Adaptable Scratch Migration Assay
08:59

A Cost Effective and Adaptable Scratch Migration Assay

Published on: June 30, 2020

5.5K
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
16:24

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

20.3K

Area of Science:

  • Biostatistics
  • Pharmacogenomics
  • Precision Medicine

Background:

  • Meta-analysis aggregates studies to identify biomarkers for drug efficacy and safety.
  • Replicating discoveries from meta-analyses, especially in pharmacogenomics genome-wide association studies (PGx GWAS), is challenging.
  • Limited power in individual studies hinders robust biomarker identification and validation.

Approach:

  • Developed MAJAR (meta-analysis of joint effect associations for biomarker replicability assessment), a novel statistical framework.
  • MAJAR jointly tests prognostic and predictive effects using an enhanced expectation-maximization algorithm.
  • Calculates posterior-probability-of-replicabilities and Bayesian false discovery rates (Fdr) for robust assessment.

Key Points:

  • MAJAR demonstrates improved statistical power and well-controlled Fdr compared to existing methods in simulations.
  • The framework shows robustness to outliers across various data generation processes.
  • MAJAR identified 12 novel variants associated with treatment-related LDL cholesterol reduction in a PGx GWAS dataset.

Conclusions:

  • MAJAR offers a powerful approach for assessing biomarker replicability in meta-analyses.
  • The framework enhances the impact of discovered biomarkers by providing reliable validation.
  • MAJAR advances precision medicine by enabling more accurate patient stratification and drug response prediction.