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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

121
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
121
Crossover Experiments01:16

Crossover Experiments

2.8K
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.
2.8K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

214
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
214

You might also read

Related Articles

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

Sort by
Same author

A FRET Aptasensor Based on N-CQDs for Detection of Cardiac Troponin I in Acute Myocardial Infarction Diagnosis.

International journal of nanomedicine·2026
Same author

Ceperognastat in Early Symptomatic Alzheimer Disease: A Randomized Clinical Trial.

JAMA·2026
Same author

RHINO: An Integrative Multi-Omics Framework Linking Circadian Physiology to Precision Medicine.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

The Impact of Climate Change on the Climatic Suitability of <i>Rhipicephalus microplus</i> in Mainland China.

Vector borne and zoonotic diseases (Larchmont, N.Y.)·2026
Same author

A Bayesian Adaptive Marker-Stratified Design for Phase II Clinical Trials Using Calibrated Spike-and-Slab priors.

Statistics in biopharmaceutical research·2026
Same author

An Adaptive Biomarker-based Umbrella Trial Design Using Bayesian Latent Class Model.

Statistics in biopharmaceutical research·2026

Related Experiment Video

Updated: Jun 23, 2025

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction
09:44

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

Published on: January 29, 2019

10.1K

T3 + 3: 3 + 3 Design With Delayed Outcomes.

Jiaying Guo1,2, Mengyi Lu3, Isabella Wan4

  • 1Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.

Pharmaceutical Statistics
|June 26, 2024
PubMed
Summary

The novel time-to-event 3+3 (T3+3) design addresses delayed outcomes in early-phase oncology trials. This efficient method accelerates drug development by shortening trial duration and improving maximum tolerated dose identification.

Keywords:
3 + 3 designBayesian adaptive designdelayed outcomeoptimal designphase I clinical trials

More Related Videos

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.0K
Implantation of Total Artificial Heart in Congenital Heart Disease
07:27

Implantation of Total Artificial Heart in Congenital Heart Disease

Published on: July 18, 2014

24.6K

Related Experiment Videos

Last Updated: Jun 23, 2025

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction
09:44

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

Published on: January 29, 2019

10.1K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.0K
Implantation of Total Artificial Heart in Congenital Heart Disease
07:27

Implantation of Total Artificial Heart in Congenital Heart Disease

Published on: July 18, 2014

24.6K

Area of Science:

  • Oncology
  • Clinical Trial Design
  • Pharmacology

Background:

  • Delayed patient outcomes are a significant challenge in phase I oncology trials, causing logistical issues and prolonging study durations.
  • Current trial designs often lead to unnecessary suspensions and resource wastage due to pending toxicity data.

Purpose of the Study:

  • To introduce and evaluate the time-to-event 3+3 (T3+3) design for phase I oncology trials.
  • To address the issue of delayed outcomes and improve the efficiency of maximum tolerated dose (MTD) identification.

Main Methods:

  • The T3+3 design utilizes actual follow-up time for patients with pending toxicity outcomes, allowing continuous patient accrual.
  • It employs isotonic regression for toxicity rate estimation across dose levels, accommodating various MTD targets.
  • The study includes comprehensive computer simulations to assess the T3+3 design's performance.

Main Results:

  • The T3+3 design substantially shortens trial duration compared to the conventional 3+3 design.
  • It demonstrates significantly higher accuracy in MTD identification than the rolling six design.
  • The design is costless, easily implementable with pretabulated rules, and requires minimal computational effort.

Conclusions:

  • The T3+3 design effectively resolves the delayed outcome problem in phase I oncology trials.
  • It retains the simplicity, transparency, and cost-effectiveness of the traditional 3+3 design.
  • This innovative design holds significant potential for accelerating early-phase drug development.