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 Experiment Videos

CrossOver: an algorithm for the construction of efficient cross-over designs.

J A John1, K G Russell, D Whitaker

  • 1Department of Statistics, University of Waikato, Hamilton, New Zealand. nye@stats.waikato.ac.nz

Statistics in Medicine
|August 19, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The Yankauer suction tube sleeve technique.

Annals of the Royal College of Surgeons of England·2021
Same author

Guideline on the peri-operative management of patients with sickle cell disease: Guideline from the Association of Anaesthetists.

Anaesthesia·2021
Same author

Optimising pain management protocols following cardiac surgery: A protocol for a national quality improvement study.

International journal of surgery protocols·2019
Same author

Prefilled insulin syringes.

Anaesthesia·2016
Same author

Should ice be used in the treatment of acute haemarthrosis in haemophilia?

Haemophilia : the official journal of the World Federation of Hemophilia·2013
Same author

Management of psoriasis in Africa and the Middle East: a review of current opinion, practice and opportunities for improvement.

The Journal of international medical research·2011
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

This study introduces an algorithm for creating efficient cross-over designs, optimizing experiments with direct and carry-over treatment effects. The algorithm ensures flexibility across various models, improving experimental planning.

Area of Science:

  • Biostatistics
  • Experimental Design
  • Statistical Modeling

Background:

  • Cross-over experiments involve sequential treatments and consider direct and carry-over effects.
  • The choice of experimental design depends heavily on the assumed carry-over effect model.
  • Optimality of a design can vary significantly with different carry-over effect models.

Purpose of the Study:

  • To develop a flexible algorithm for constructing efficient cross-over designs.
  • To accommodate a range of models including direct treatment effects and various carry-over effects.
  • To provide a robust method for experimental design in the presence of complex treatment interactions.

Main Methods:

  • An algorithm is described for generating efficient cross-over designs.

Related Experiment Videos

  • The algorithm considers direct treatment effects and various functions of carry-over effects.
  • Performance is assessed against existing designs and models in the literature.

    • Main Results:

    • The developed algorithm demonstrates effectiveness and flexibility.
    • It successfully constructs efficient designs for a spectrum of carry-over effect models.
    • The algorithm's performance is validated through comparisons with established literature.

    Conclusions:

    • The new algorithm offers a versatile approach to designing cross-over experiments.
    • It provides efficient designs adaptable to different carry-over effect assumptions.
    • This method enhances the reliability and applicability of cross-over study designs.