Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Randomized Experiments

Randomized Experiments

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...

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

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 subjects...

Blinding

Blinding

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.

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...

Crossover Experiments

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.

You might also read

Related Articles

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

Sort by

Same author

Patient Versus Prediction-Level Evaluation of a Dynamic Clinical Prediction Model of Sepsis.

Research square·2026

Same author

Effect of Using Personalized Estimates of Diabetes Risk During Primary Care Visits for People With Prediabetes.

Learning health systems·2026

Same author

Patient Versus Prediction-Level Evaluation of a Dynamic Clinical Prediction Model of Sepsis.

medRxiv : the preprint server for health sciences·2026

Same author

Mechanism Attribution After Cryptogenic Stroke: Toward a RoPE Score for Atrial Fibrillation Detected After Stroke.

Stroke·2026

Same author

Diagnostic Agreement and Prognostic Value of Clinically Interpreted CT vs MRI for Incidentally Discovered Covert Cerebrovascular Disease.

Neurology·2026

Same author

Conducting multicenter trials through the trial innovation network comprehensive consultation.

Contemporary clinical trials·2026

Same journal

Population Pharmacokinetic, Exposure-Response Efficacy and Safety Analyses of Favezelimab in Patients With Solid Tumors.

Clinical and translational science·2026

Same journal

Development of a Biphasic Modified-Release Ibuprofen Tablet Formulation: Single-Dose and Multiple-Dose Pharmacokinetics and Food-Effect Studies.

Clinical and translational science·2026

Same journal

Circulating M-MDSC Expansion During Therapy Associates With Treatment Response in Multiple Myeloma: A Longitudinal Observational Study.

Clinical and translational science·2026

Same journal

PBPK Modeling and Clinical Data Reveal Reduced Impact of CYP3A4 and CYP2C9 Inhibitors on Elimination of Siponimod.

Clinical and translational science·2026

Same journal

Drug Interactions of Vebreltinib, a Novel Type I c-Met Inhibitor, Coadministration With Rifampin or Itraconazole in Healthy Participants.

Clinical and translational science·2026

Same journal

Correction to "Severe Thiopurine-Induced Myelosuppression in a Pediatric Acute Lymphoblastic Leukemia Patient ith the NUDT15 1/6 Genotype: A Brief Report".

Clinical and translational science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

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

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

Published on: January 8, 2020

Random treatment assignment using mathematical equipoise for comparative effectiveness trials.

Harry P Selker¹, Robin Ruthazer, Norma Terrin

¹Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA. hselker@tuftsmedicalcenter.org

Clinical and Translational Science

|February 26, 2011

Summary

This summary is machine-generated.

Random assignment in clinical trials needs equipoise. Predictive models like PCI-TPI can identify patient-specific equipoise, supporting enrollment in comparative effectiveness trials for ST-elevation myocardial infarction (STEMI).

More Related Videos

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Published on: November 21, 2025

Related Experiment Videos

Last Updated: Jun 4, 2026

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

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

Published on: January 8, 2020

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Published on: November 21, 2025

Area of Science:

Clinical Trials
Cardiology
Medical Informatics

Background:

Random assignment in clinical trials requires equipoise, where no treatment is clearly superior.
Prior trial data may suggest treatment superiority, seemingly negating equipoise.
Patient-specific factors can create equipoise even when group averages suggest otherwise.

Purpose of the Study:

To evaluate if real-time, patient-specific predictions can determine equipoise for trial enrollment.
To assess the utility of the Percutaneous Coronary Intervention Thrombolytic Predictive Instrument (PCI-TPI) in this context.
To explore the application of predictive modeling in comparative effectiveness research.

Main Methods:

Utilized the PCI-TPI model, which predicts 30-day mortality for ST-elevation myocardial infarction (STEMI) patients under thrombolytic therapy (TT) versus percutaneous coronary intervention (PCI).
Estimated uncertainty in predicted treatment benefit differences using model coefficient uncertainty.
Analyzed the distribution of predicted benefits for individual patients within the PCI-TPI development dataset (n=2,781).

Main Results:

In three typical clinical scenarios, randomization was deemed potentially warranted for 70%, 93%, and 80% of patients.
Patient-specific predictions revealed a distribution of benefits, indicating equipoise for a significant proportion of individuals.
Uncertainty estimation supported the justification for randomization in specific patient profiles.

Conclusions:

Predictive models can facilitate real-time, patient-specific determination of equipoise, justifying trial enrollment.
This approach enhances the design and execution of comparative effectiveness trials.
It aids in applying clinical trial findings to individual patient care decisions.