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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

286
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...
286
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
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...
3.4K
Cancer Survival Analysis01:21

Cancer Survival Analysis

455
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
455
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

554
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
554
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

174
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,...
174
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.7K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.7K

You might also read

Related Articles

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

Sort by
Same author

An Alternative Treatment Effect Measure for Time-to-Event Oncology Randomized Trials.

Cancers·2025
Same author

Robust Permutation Test of Intraclass Correlation Coefficient for Assessing Agreement.

Cancers·2025
Same author

Strategies to boost statistical efficiency in randomized oncology trials with primary time-to-event endpoints.

Statistical methods in medical research·2025
Same author

Consequences of the perivascular niche remodeling for tumoricidal T-cell trafficking into metastasis of ovarian cancer.

Research square·2024
Same author

The Cancer Moonshot Immuno-Oncology Translational Network at 5: accelerating cancer immunotherapies.

Journal of the National Cancer Institute·2023
Same author

Computational Optimization of Irradiance and Fluence for Interstitial Photodynamic Therapy Treatment of Patients with Malignant Central Airway Obstruction.

Cancers·2023

Related Experiment Video

Updated: Sep 11, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

376

Optimizing One-Sample Tests for Proportions in Single- and Two-Stage Oncology Trials.

Alan David Hutson1

  • 1Roswell Park Comprehensive Cancer Center, Department of Biostatistics and Bioinformatics, Elm and Carlton Streets, Buffalo, NY 14623, USA.

Cancers
|August 14, 2025
PubMed
Summary

A new convolution-based method improves early-phase oncology trial designs by reducing sample sizes and costs. This approach offers precise Type I error control, making clinical trials more efficient and flexible.

Keywords:
clinical trialexact binomial testperturbation testsmall-sample power

More Related Videos

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
09:32

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Published on: February 8, 2018

14.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

194

Related Experiment Videos

Last Updated: Sep 11, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

376
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
09:32

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Published on: February 8, 2018

14.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

194

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Oncology Research

Background:

  • Phase II oncology trials frequently use single-arm designs due to cost or rarity of diseases.
  • Traditional methods like exact binomial tests and Simon's two-stage designs are often conservative, leading to lower actual Type I error rates than nominal alpha.
  • This conservativeness can result in inefficient trial designs with larger sample sizes than necessary.

Purpose of the Study:

  • To develop a novel, flexible, and efficient method for early-phase oncology trial design.
  • To maintain accurate Type I error control while improving design efficiency.
  • To offer a practical alternative to existing conservative trial design methods.

Main Methods:

  • A convolution-based statistical method is proposed, combining binomial and simulated normal distributions.
  • This method constructs an unbiased estimator for the true response rate (π).
  • Theoretical properties are derived, and performance is evaluated against traditional exact tests in one-stage and two-stage designs.

Main Results:

  • The proposed method yields more efficient trial designs with reduced sample sizes compared to standard approaches.
  • Type I error rates are precisely controlled, matching the nominal alpha level.
  • A new two-stage design with interim futility analysis is introduced, demonstrating significant reductions in trial cost and duration.

Conclusions:

  • The convolution-based approach provides a flexible and efficient alternative for early-phase oncology trial design.
  • It effectively addresses the conservativeness of traditional methods.
  • The method offers practical advantages, including reduced resource utilization and shorter study timelines.