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

6.9K
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...
6.9K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

126
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,...
126
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

546
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
546
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

179
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
179
Hazard Ratio01:12

Hazard Ratio

116
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
116
Blinding01:11

Blinding

2.4K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
2.4K

You might also read

Related Articles

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

Sort by
Same author

Optimal designs for discrete-time survival models with competing risks.

Lifetime data analysis·2026
Same author

Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs.

Clinical trials (London, England)·2025
Same author

Design optimization of longitudinal studies using metaheuristics: Application to lithium pharmacokinetics.

Statistical methods in medical research·2025
Same author

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications.

Journal of medical Internet research·2023
Same author

Optimal designs for health risk assessments using fractional polynomial models.

Stochastic environmental research and risk assessment : research journal·2022
Same author

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis.

Statistics in medicine·2022
Same journal

A Bayesian Optimal Interval Design Considering Efficacy and Toxicity in Early Phase Basket Trials.

Pharmaceutical statistics·2026
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

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

14.5K

Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real

Alan R Vazquez1, Weng-Kee Wong2

  • 1School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico.

Pharmaceutical Statistics
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

Modern clinical trial randomization uses adaptive methods. Mathematical programming enhances adaptive randomization, balancing subject covariates and group sizes, outperforming traditional methods in small trials.

Keywords:
covariate‐adaptive trialenergy distanceminimization methodprior information

More Related Videos

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
00:04

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

10.7K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Related Experiment Videos

Last Updated: Jun 28, 2025

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

14.5K
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
00:04

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

10.7K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Adaptive randomization is standard in modern clinical trials, using accumulated data for subject assignment.
  • Mathematical programming offers advanced adaptive methods to balance trial groups by size and covariate distributions.
  • Existing covariate-adaptive randomization methods have limitations, especially in small trials.

Purpose of the Study:

  • To review and compare mathematical programming-based adaptive randomization methods with common covariate-adaptive methods.
  • To introduce a novel energy distance measure for assessing group discrepancy based on joint covariate distributions.
  • To demonstrate the superiority of mathematical programming methods using this new metric.

Main Methods:

  • Review of adaptive randomization techniques, focusing on mathematical programming approaches.
  • Introduction of an energy distance metric to quantify covariate distribution discrepancies between groups.
  • Numerical experiments comparing mathematical programming methods against standard covariate-adaptive methods.

Main Results:

  • Mathematical programming methods demonstrate significant advantages in balancing subject covariates.
  • The proposed energy distance measure provides a more comprehensive assessment of group balance than marginal distribution comparisons.
  • Numerical experiments confirm the effectiveness of mathematical programming under the new energy distance metric.

Conclusions:

  • Mathematical programming-based adaptive randomization offers superior control over group balance in clinical trials.
  • The energy distance measure is a valuable tool for evaluating the performance of randomization methods.
  • These advanced methods are particularly beneficial for small clinical trials, improving study validity.