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

Crossover Experiments01:16

Crossover Experiments

4.5K
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.
4.5K
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

147
Body: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...
147
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

192
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...
192
Randomized Experiments01:13

Randomized Experiments

8.8K
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...
8.8K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.8K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.8K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

641
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
641

You might also read

Related Articles

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

Sort by
Same author

Integrating Multiple Clustering Techniques and Performance Measures via Ranking for scRNA-Seq Data.

Statistics in medicine·2025
Same author

Personalized Treatment Selection for Multivariate Ordinal Scale Outcomes and Multiple Treatments.

Pharmaceutical statistics·2025
Same author

Can the Unit Size Predict Outcomes? Testing for Informativeness in Three-Level Designs.

Statistics in medicine·2025
Same author

Comparing two hazard curves when there is a treatment time-lag effect.

Statistics in medicine·2024
Same author

Testing for marginal covariate effect when the subgroup size induced by the covariate is informative.

Statistical methods in medical research·2024
Same author

Multiblock partial least squares and rank aggregation: Applications to detection of bacteriophages associated with antimicrobial resistance in the presence of potential confounding factors.

Statistics in medicine·2024

Related Experiment Video

Updated: Jan 5, 2026

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

15.0K

Personalized treatment selection using data from crossover designs with carry-over effects.

Chathura Siriwardhana1, K B Kulasekera2, Somnath Datta3

  • 1Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii.

Statistics in Medicine
|October 23, 2019
PubMed
Summary

This study introduces a new semiparametric method for optimal treatment selection using patient covariates in crossover trials with carry-over effects. The method accurately assigns treatments by comparing outcome probabilities, enhancing patient-specific care.

Keywords:
crossover designsdesign variablespersonalized treatmentsprecision medicinesingle-index models

More Related Videos

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
03:05

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

Published on: November 21, 2025

426
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

8.6K

Related Experiment Videos

Last Updated: Jan 5, 2026

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

15.0K
Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
03:05

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

Published on: November 21, 2025

426
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

8.6K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Crossover designs are efficient for clinical trials but can be complicated by carry-over effects.
  • Nonparametric methods for carry-over effects may limit the use of joint patient measurements for treatment comparison.
  • Estimating optimal patient-specific treatments requires methods that account for individual covariates and treatment carry-over.

Purpose of the Study:

  • To develop a semiparametric method for estimating optimal patient treatment in crossover trials with carry-over effects.
  • To address limitations of nonparametric methods in comparing treatments when carry-over is present.
  • To propose a robust approach for personalized treatment assignment using covariate information.

Main Methods:

  • A semiparametric approach is proposed for optimal treatment estimation.
  • The method compares probabilities of treatment dominance based on patient-specific scores derived from covariates.
  • Single-index models are utilized to link outcome variables with covariates.

Main Results:

  • The proposed method demonstrates highly accurate frequencies of correct treatment assignments.
  • Empirical investigations confirm the effectiveness of the approach in identifying optimal treatments.
  • The method shows robustness against deviations from the single-index model structure.

Conclusions:

  • The semiparametric method provides an effective way to estimate optimal treatments in crossover designs with carry-over effects.
  • The approach overcomes limitations of traditional methods by utilizing joint patient measurements.
  • Real data analysis validates the practical applicability of the proposed procedure for personalized medicine.