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

Randomized Experiments01:13

Randomized Experiments

8.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.2K
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
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...
6.6K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

201
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,...
201
Bias01:22

Bias

6.4K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
6.4K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

19
Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
19
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

809
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
809

You might also read

Related Articles

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

Sort by
Same author

Room-Temperature Tuning and Probing of Fermi Polarons in Atomically Thin Semiconductors on a Plasmonic Metasurface.

ACS nano·2026
Same author

Deciphering Polyphenol Interactions with Poly(<sub>L</sub>-proline) and Polysarcosine.

Biomacromolecules·2026
Same author

DNA damage complexity as a predictor of cell survival: a microscopic Monte Carlo-based modeling framework for photon, proton and carbon ion irradiation.

Physics in medicine and biology·2026
Same author

Charge-Engineered COFs for Biointegrated Memristor Nerves.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

A fast dosimetric optimization method of intensity modulated brachytherapy (IMBT) treatment plans for cervical cancer.

Physics in medicine and biology·2026
Same author

Beyond Fixation: Persistent Genetic Variation Under Intense Selection.

bioRxiv : the preprint server for biology·2026
Same journal

DataAtlas: automatic generation of data dictionaries using large language models.

JAMIA open·2026
Same journal

An examination of the availability and characteristics of social needs data in the electronic health records: a path to social data harmonization and standardization at Johns Hopkins medicine.

JAMIA open·2026
Same journal

Generative artificial intelligence implementation in REDCap.

JAMIA open·2026
Same journal

Improving readability of layperson abstracts and summaries in oncology using task-specific large language model powered tool: results from the BRIDGE-AI 7 study.

JAMIA open·2026
Same journal

Accuracy of administrative data in ascertaining health conditions: a systematic review.

JAMIA open·2026
Same journal

Building a consumer health informatics introductory course consensus curriculum: an eDelphi study.

JAMIA open·2026
See all related articles

Related Experiment Video

Updated: Oct 19, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics.

Miao Qi1, Owen Cahan2, Morgan A Foreman3

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA.

JAMIA Open
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces machine learning fairness metrics to identify underrepresented groups in clinical trials. These tools help assess and improve the generalizability and health equity of randomized clinical trials.

Keywords:
health equitymachine learningpopulation representativenessrandomized clinical trialssubgroup

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Related Experiment Videos

Last Updated: Oct 19, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Area of Science:

  • Clinical Trials Methodology
  • Health Equity Research
  • Machine Learning Applications

Background:

  • Randomized clinical trials (RCTs) are crucial for evidence-based medicine.
  • Underrepresentation of specific subpopulations in RCTs can limit generalizability and exacerbate health inequities.
  • Existing methods for assessing cohort representativeness may not be sufficiently standardized or intuitive.

Purpose of the Study:

  • To develop novel metrics for quantifying population representativeness in RCTs.
  • To frame the problem of RCT cohort underrepresentation as a machine learning (ML) fairness challenge.
  • To create user-friendly tools for visualizing and analyzing subgroup representation in clinical trials.

Main Methods:

  • Formulated RCT cohort representativeness as an ML fairness problem.
  • Derived new ML fairness metrics based on enrollment fractions and subpopulation rates.
  • Calculated standardized metrics efficiently using data from RCT cohorts and target populations.
  • Deployed metrics in an interactive visualization tool for analyzing RCTs against target populations (e.g., NHANES data for diabetes and hypertension).

Main Results:

  • Demonstrated the ability of proposed metrics to rapidly assess representativeness across diverse subpopulations (gender, race, ethnicity, smoking status, blood pressure).
  • Showcased how standardized metrics provide an intuitive scale for evaluating representation, even with varying enrollment fractions.
  • Illustrated the utility of the interactive tool in analyzing specific RCTs against defined target populations.

Conclusions:

  • Normalized metrics offer a standardized approach to evaluate subgroup representation in RCTs, complementing existing methods.
  • Quantifying representational gaps supports the evaluation of RCT generalizability and promotes health equity.
  • The interactive tool facilitates the identification of underrepresented subgroups for targeted interventions and improved trial design.