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

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.5K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.5K
Binomial Probability Distribution01:15

Binomial Probability Distribution

12.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
12.4K
Bonferroni Test01:10

Bonferroni Test

2.9K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

3.2K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
3.2K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

366
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
366
Blinding01:11

Blinding

3.4K
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.
3.4K

You might also read

Related Articles

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

Sort by
Same author

CT Perfusion Blood Flow Provides an Early Response Marker for Progression-free Survival in a Prospective Randomized Clinical Trial of Metastatic Renal Cell Carcinoma.

Technology in cancer research & treatment·2026
Same author

Propensity Score-Matched External Controls in Rare Disease Trials: A Promising Tool Requiring Rigorous Validation.

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

Pretreatment Risk Model for Radiation-Induced Lymphopenia Is Associated With Adjuvant Durvalumab Efficacy in Patients With Unresectable Stage III NSCLC.

International journal of radiation oncology, biology, physics·2026
Same author

The Impact of Immunotherapy on Incidence of Second Primary Malignancies: A Surrogate for Antitumor Surveillance Activation.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

PD-L1 subpopulations of metastatic urothelial carcinoma demonstrate heterogeneity to chemotherapy: an integrated analysis of digitized trial results.

Cancer metastasis reviews·2026
Same author

Master Protocol Design With Hybrid Control for Efficient Early-Phase Trial Consolidation.

JCO precision oncology·2025
Same journal

A statistical evaluation of decision-making methods and the efficiency of Bayesian multi-arm multi-stage trials.

Clinical trials (London, England)·2026
Same journal

Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.

Clinical trials (London, England)·2026
Same journal

Phase I design for partially ordered groups with late-onset toxicity.

Clinical trials (London, England)·2026
Same journal

Trial informed consent forms, the Declaration of Helsinki and the SPIRIT 2025 statement.

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Morning panel discussion).

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
See all related articles

Related Experiment Video

Updated: Oct 4, 2025

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

33.7K

Bayesian basket trial design with false-discovery rate control.

Emily C Zabor1, Michael J Kane2, Satrajit Roychoudhury3

  • 1Cleveland Clinic, Cleveland, OH, USA.

Clinical Trials (London, England)
|February 7, 2022
PubMed
Summary
This summary is machine-generated.

Tumor-agnostic therapies use molecular targets, advancing oncology beyond traditional classification. This study introduces a multisource exchangeability model for phase II basket trials, improving power with false-discovery rate control, especially for small sample sizes.

Keywords:
Adaptive designBayesianclinical trialsmaster protocolsprecision oncologytargeted therapytumor agnostic

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.7K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

42.5K

Related Experiment Videos

Last Updated: Oct 4, 2025

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

33.7K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.7K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

42.5K

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Tumor-agnostic therapies target molecular alterations, transcending traditional cancer histology.
  • Master protocol designs, like basket trials, are crucial for developing targeted therapies across diverse cancer types.
  • Basket trials present complex design challenges, including managing multiple testing, necessitating guidance for investigators.

Purpose of the Study:

  • To explore the sensitivity of the multisource exchangeability model to prior specifications under varying response heterogeneity.
  • To present a multisource exchangeability model design incorporating false-discovery rate (FDR) control for phase II basket trials.
  • To compare the operating characteristics of FDR control versus family-wise error rate (FWER) control and frequentist independent basket analysis.

Main Methods:

  • Simulations were conducted using a multisource exchangeability model, assessing prior probability of exchangeability.
  • A novel design incorporating FDR control was developed and compared to FWER control and independent analysis.
  • Simulations were based on the SUMMIT trial design for Neratinib in solid tumors, focusing on single-arm phase II trials with binary outcomes.

Main Results:

  • Prior probabilities of exchangeability between 0.1 and 0.3 offered optimal precision-bias trade-offs, particularly for baskets with <30 samples.
  • Re-analysis of the SUMMIT trial demonstrated the breast cancer basket exceeded null response rates (posterior probability 0.999) with low exchangeability to other baskets.
  • FDR control significantly improved power in small-sample baskets compared to FWER control (e.g., 0.76 vs. 0.56 power for a single active basket with n=25).

Conclusions:

  • Calibrated prior exchangeability probabilities and FDR control enhance multisource exchangeability model designs for phase II basket trials.
  • These methods provide high power to detect promising treatments, even with small sample sizes in individual baskets.
  • The findings offer crucial guidance for optimizing complex basket trial designs in precision oncology.