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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

121
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,...
121
Censoring Survival Data01:09

Censoring Survival Data

65
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
65
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

102
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
102
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

155
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...
155
Survival Curves01:18

Survival Curves

106
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
106
Actuarial Approach01:20

Actuarial Approach

63
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
63

You might also read

Related Articles

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

Sort by
Same author

BAR12: Bayesian Autoregressive Phase 1-2 Design for Cell Therapy Trials With Manufacturing Changes.

Statistics in medicine·2026
Same author

High-Dose Chemotherapy for Multiply or Poor-Risk Relapsed Germ Cell Tumors.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Precision generalized phase I-II designs.

Biometrics·2025
Same author

Practical Bayesian Guidelines for Small Randomized Oncology Trials.

Cancers·2025
Same author

Enhancement of High-Dose Chemotherapy and Autologous SCT with the PARP Inhibitor Olaparib for Refractory Lymphoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Impact of postprogression therapies on overall survival: Recommendations from the 2023 kidney cancer association think tank meeting.

Urologic oncology·2024
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Bayesian Safety and Futility Monitoring in Phase II Trials Using One Utility-Based Rule.

Juhee Lee1, Peter F Thall2

  • 1Department of Statistics, University of California, Santa Cruz, Santa Cruz, California.

Statistics in Medicine
|November 5, 2024
PubMed
Summary
This summary is machine-generated.

A new Bayesian method, U-Bayes, improves early stopping rules in clinical trials by using joint ordinal outcomes instead of dichotomizing data. This approach enhances treatment acceptability decisions by preserving information and considering outcome associations.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

5.8K

Related Experiment Videos

Last Updated: Jun 8, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

5.8K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Decision Theory

Background:

  • Phase II clinical trials often dichotomize ordinal toxicity and response data for monitoring.
  • Conventional methods using cut-points lead to information loss and biased treatment acceptability decisions.
  • Existing approaches ignore the crucial association between toxicity and response outcomes.

Purpose of the Study:

  • To introduce a novel Bayesian method (U-Bayes) for more accurate treatment acceptability assessment in phase II trials.
  • To overcome the limitations of dichotomization in analyzing joint ordinal outcomes.
  • To develop an early stopping rule that utilizes the full joint distribution of outcomes.

Main Methods:

  • Proposed a Bayesian approach (U-Bayes) using elicited numerical utilities of joint ordinal outcomes.
  • Constructed a single early stopping rule based on comparing the mean utility to a lower limit.
  • Developed a step-by-step algorithm for U-Bayes rule construction using elicited utilities and marginal probability limits.

Main Results:

  • U-Bayes avoids information loss by not dichotomizing ordinal outcomes.
  • The method accounts for the association between toxicity and response.
  • Simulation studies demonstrated U-Bayes significantly improves the probability of correctly determining treatment acceptability compared to conventional designs.

Conclusions:

  • U-Bayes offers a superior alternative to conventional methods for phase II clinical trial monitoring.
  • The Bayesian approach enhances decision-making accuracy by leveraging the complete joint distribution of ordinal outcomes.
  • This method provides a more robust framework for evaluating experimental treatments based on toxicity and response.