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

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
Data Validation01:15

Data Validation

183
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:
183
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
Measures of Intelligence01:29

Measures of Intelligence

7.6K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
7.6K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Variability: Analysis01:11

Variability: Analysis

158
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
158

You might also read

Related Articles

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

Sort by
Same author

Finerenone in Patients With Chronic Kidney Disease Due to Glomerular Diseases: A Randomized Clinical Trial.

JAMA·2026
Same author

Finerenone in Persons with Chronic Kidney Disease without Diabetes.

The New England journal of medicine·2026
Same author

Estimating the minimal important change of single-item measures using the adjusted predictive modeling method or the longitudinal confirmatory factor analysis method.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026
Same author

Framework for Analytical Validation of DHT-Based Actigraphy and Signal Measures in HF Trials: The VALIDATE-HF Program.

JACC. Heart failure·2025
Same author

Blood pressure, safety and clinical efficacy of vericiguat in chronic heart failure with reduced ejection fraction: Insights from the VICTOR trial.

European journal of heart failure·2025
Same author

Effect of Vericiguat on Total Heart Failure Events in Compensated Outpatients With HFrEF: Insights From VICTOR.

Journal of the American College of Cardiology·2025

Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

802

Clinical Validation of Novel Digital Measures: Statistical Methods for Reliability Evaluation.

Bohdana Ratitch1, Andrew Trigg2, Madhurima Majumder3

  • 1Statistics and Data Insights, Bayer Inc., Mississauga, ON, Canada.

Digital Biomarkers
|August 17, 2023
PubMed
Summary

Reliability assessment is crucial for validating digital health tools. This study offers statisticians and data scientists a unified overview of statistical methods for evaluating the reliability of digital clinical measures.

Keywords:
Clinical validationDigital health technologyReliabilityStatistical methods

More Related Videos

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

552
Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

3.4K

Related Experiment Videos

Last Updated: Jul 19, 2025

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

802
Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

552
Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

3.4K

Area of Science:

  • Biostatistics
  • Digital Health Technology
  • Clinical Measurement Validation

Background:

  • Reliability assessment is a critical step in validating digital health technology (DHT) tools for clinical research and decision-making.
  • It helps characterize the signal-to-noise ratio and measurement error, indicating the potential usefulness of a clinical measure.
  • Current methodologies for reliability analyses are dispersed across various validation literature.

Purpose of the Study:

  • To provide a comprehensive overview of statistical methodologies and analytical tools for reliability assessment of novel digital clinical measures.
  • To consolidate scattered reliability analysis techniques into a generalizable framework for DHT validation.
  • To equip statisticians and data scientists with the necessary tools for robust digital clinical measure validation.

Main Methods:

  • Review of general modeling frameworks and statistical metrics for reliability assessment in clinical validation.
  • Presentation of methods for assessing agreement and measurement error.
  • Adaptation of techniques for categorical measures and illustration with wearable device accelerometer data.

Main Results:

  • A unified framework for reliability assessment applicable to digital clinical measures.
  • Demonstration of techniques using real-world physical activity data from a clinical trial.
  • Identification of key statistical metrics and analytical tools for DHT validation.

Conclusions:

  • Standardized reliability assessment is essential for the successful implementation of digital clinical measures.
  • This paper serves as a valuable resource for statisticians and data scientists developing and validating DHTs.
  • The presented methodologies enhance the rigor and trustworthiness of evidence generated by digital health tools.