Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Accelerators

Accelerators

Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
The effectiveness of calcium chloride can...

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...

Trial and Error and Algorithm

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...

Clinical Trials: Overview

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...

Clinical Trials

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...

You might also read

Related Articles

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

Sort by

Same author

Statistical analysis plan considerations for autologous cell and cell-based gene therapy clinical trials.

Journal of biopharmaceutical statistics·2026

Same author

Overview of dose-finding designs and trials in cell and gene therapies.

Journal of biopharmaceutical statistics·2026

Same author

Testing rates and outcomes in <i>Clostridioides difficile</i> infection between REM (racially and ethnically minoritized) and non-REM patients.

Infection control and hospital epidemiology·2026

Same author

Exploiting vitamin C as a prooxidant to activate ROS-responsive prodrugs for potent and selective tumor killing.

Redox biology·2026

Same author

Pivotal trial design considerations for new and next generation cell and gene therapies.

Journal of biopharmaceutical statistics·2026

Same author

Effects of the 2016 CDC opioid prescribing guideline: a scoping review.

BMJ open·2026

Search research articles

Related Experiment Video

Updated: Feb 10, 2026

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Time-to-Event Bayesian Optimal Interval Design to Accelerate Phase I Trials.

Ying Yuan¹, Ruitao Lin^2,3, Daniel Li⁴

¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas. yyuan@mdanderson.org daniel.li@junotherapeutics.com.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research

|May 18, 2018

Summary

The novel time-to-event Bayesian optimal interval (TITE-BOIN) design accelerates early-phase cancer trials. It enables real-time dose decisions for new patients, even with pending toxicity data, improving safety and accuracy.

More Related Videos

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

Published on: August 23, 2011

Fracture Apparatus Design and Protocol Optimization for Closed-stabilized Fractures in Rodents

Fracture Apparatus Design and Protocol Optimization for Closed-stabilized Fractures in Rodents

Published on: August 14, 2018

Related Experiment Videos

Last Updated: Feb 10, 2026

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

Published on: August 23, 2011

Fracture Apparatus Design and Protocol Optimization for Closed-stabilized Fractures in Rodents

Fracture Apparatus Design and Protocol Optimization for Closed-stabilized Fractures in Rodents

Published on: August 14, 2018

Area of Science:

Oncology
Clinical Trial Design
Pharmacology

Background:

Late-onset toxicity is a significant challenge for novel molecularly targeted agents and immunotherapies.
Existing adaptive phase I trial designs struggle with logistic difficulties caused by late-onset toxicity and rapid patient accrual.
Timely toxicity observation is crucial for dose-escalation rules in current trial designs.

Purpose of the Study:

To introduce and evaluate the time-to-event Bayesian optimal interval (TITE-BOIN) design for accelerating phase I clinical trials.
To address the logistic challenges posed by late-onset toxicity in adaptive trial designs.
To enhance the efficiency and accuracy of identifying the maximum tolerated dose (MTD).

Main Methods:

The TITE-BOIN design allows for real-time dose assignment decisions for new patients while pending toxicity data from enrolled patients is still being collected.
Dose-escalation/deescalation rules are pre-tabulated, ensuring transparency and ease of implementation, similar to the rolling six design.
The TITE-BOIN design offers flexibility in selecting the target dose-limiting toxicity (DLT) rate and improved MTD identification accuracy compared to other methods.

Main Results:

The TITE-BOIN design demonstrates comparable accuracy to the time-to-event continuous reassessment method (TITE-CRM) in identifying the MTD.
TITE-BOIN offers superior overdose control compared to TITE-CRM, while TITE-CRM is more aggressive in dose escalation, potentially underdosing fewer patients.
When no data is pending, TITE-BOIN seamlessly reverts to the standard BOIN design, ensuring continuity.

Conclusions:

The TITE-BOIN design facilitates continuous patient accrual without compromising patient safety or the accuracy of MTD identification.
This design has significant potential to accelerate early-phase drug development by overcoming logistic hurdles.
TITE-BOIN provides a simpler, more accurate, and safer alternative for phase I trial dose escalation.