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

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

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...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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...
Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

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

Sort by
Same author

Nondistributive social factors, noneconomic distributive factors.

The American journal of bioethics : AJOB·2015
Same author

Beyond bioethics: the 5th International Philosophy of Medicine Roundtable.

Theoretical medicine and bioethics·2015
Same author

Locating the right rationale: phase I.

The American journal of bioethics : AJOB·2014
Same author

Pulling the plug on clinical equipoise: a critique of Miller and Weijer.

Kennedy Institute of Ethics journal·2008
Same author

Community-equipoise and the ethics of randomized clinical trials.

Bioethics·1995
Same author

Teaching scientific integrity.

The Centennial review·1994

Related Experiment Video

Updated: Jul 15, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

So-called "clinical equipoise" and the argument from design.

Fred Gifford1

  • 1Department of Philosophy, Michigan State University, East Lansing, Michigan 48824, USA. gifford@msu.edu

The Journal of Medicine and Philosophy
|April 25, 2007
PubMed
Summary

Freedman's clinical equipoise criterion is insufficient for justifying randomized clinical trials. This approach conflates distinct equipoise concepts, masking flaws and ignoring the patient versus policy decision distinction.

Related Experiment Videos

Last Updated: Jul 15, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Area of Science:

  • Medical ethics
  • Clinical trial design
  • Research methodology

Background:

  • The concept of clinical equipoise is widely used to justify randomized clinical trials (RCTs).
  • Freedman's formulation of clinical equipoise has been particularly influential.
  • However, significant ethical and methodological concerns have been raised regarding its adequacy.

Purpose of the Study:

  • To critically review and expand upon arguments challenging the moral legitimacy of using clinical equipoise to justify RCTs.
  • To explain the reasons behind the continued acceptance of the clinical equipoise approach despite its flaws.
  • To highlight the misleading framing and conflation of concepts within Freedman's original discussion.

Main Methods:

  • Critical analysis of existing arguments concerning clinical equipoise.
  • Review of Freedman's original formulation and its implications.
  • Examination of the conflation between clinical equipoise and community equipoise.
  • Distinction between individual patient decisions and policy decisions in research ethics.

Main Results:

  • Freedman's clinical equipoise criterion is inadequate as a guide for the moral legitimacy of RCTs.
  • The approach conflates distinct versions of clinical and community equipoise, creating a misleading impression of a unified solution.
  • Crucial distinctions, such as that between individual patient and policy decisions, are overlooked, obscuring fundamental flaws.

Conclusions:

  • The clinical equipoise framework, as presented by Freedman, is ethically and methodologically flawed.
  • A more rigorous and nuanced ethical framework is needed for the justification of randomized clinical trials.
  • Attention must be paid to the distinction between individual patient consent and broader policy implications in research ethics.