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

364
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...
364
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

352
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...
352
Crossover Experiments01:16

Crossover Experiments

4.7K
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.
4.7K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

688
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...
688
Bioavailability Study Design: Single Versus Multiple Dose Studies01:11

Bioavailability Study Design: Single Versus Multiple Dose Studies

311
Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...
311
Group Design02:01

Group Design

11.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
11.0K

You might also read

Related Articles

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

Sort by
Same author

Treatment Effect Reanalysis of the Randomized Individual Screening Trial of Innovative Glioblastoma Therapy in Newly Diagnosed Glioblastoma With External Control Data.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Risk-adapted therapy guided by human papillomavirus (HPV) circulating tumor DNA in HPV-positive oropharyngeal cancer (ReACT 1.0): an exploratory phase II trial.

Nature communications·2026
Same author

Myeloma Precursors Erode Durable Immunity: Results of the IMPACT study.

Research square·2026
Same author

Ciltacabtagene autoleucel in high-risk smoldering multiple myeloma: the CAR-PRISM phase 2 trial.

Nature medicine·2026
Same author

Daratumumab in high-risk MGUS and low-risk smoldering myeloma: results of the Phase II D-PRISM study.

Nature communications·2026
Same author

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026

Related Experiment Video

Updated: Mar 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials.

Yanxun Xu1, Lorenzo Trippa2, Peter Müller3

  • 1Division of Statistics and Scientific Computing, The University of Texas at Austin, Austin, TX, U.S.A.

Statistics in Biosciences
|September 13, 2016
PubMed
Summary

Subgroup-based adaptive designs (SUBA) identify patient subgroups and adaptively assign treatments during clinical trials. This approach aims to improve targeted cancer therapy effectiveness by matching patients to optimal treatments within identified subgroups.

Keywords:
Adaptive designsBayesisan inferenceBiomarkersPosteriorSubgroup identificationTargeted therapies

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
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

775

Related Experiment Videos

Last Updated: Mar 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
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

775

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Biomarker-driven targeted therapies are crucial in cancer research, but identifying responsive patient subgroups remains challenging.
  • Current treatments often apply to broad patient populations, leading to unpredictable responses and limited efficacy for many.
  • Effective targeted therapies for specific subgroups, like HER2+ breast cancer, are rare.

Purpose of the Study:

  • To introduce Subgroup-Based Adaptive designs (SUBA) for simultaneously identifying prognostic subgroups and adaptively allocating patients to optimal treatments.
  • To address the limitations of current targeted therapy approaches in clinical trials.

Main Methods:

  • SUBA employs a random partition model for continuous patient subgroup reclassification.
  • Adaptive patient allocation is based on posterior predictive probabilities to the best subgroup-specific treatment.
  • The SUBA design was compared against equal randomization, outcome-adaptive randomization, and a probit regression design via simulation studies.

Main Results:

  • Simulation studies indicated that SUBA performs favorably compared to alternative trial designs.
  • SUBA demonstrated effectiveness in simultaneously identifying subgroups and optimizing treatment allocation.

Conclusions:

  • Subgroup-Based Adaptive designs offer a promising approach to enhance the efficiency and effectiveness of targeted cancer therapy clinical trials.
  • SUBA has the potential to improve patient outcomes by ensuring more precise treatment assignments based on identified prognostic subgroups.