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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

39
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
39
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

126
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,...
126
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

424
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
424
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

125
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.
125
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
53
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

135
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,...
135

You might also read

Related Articles

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

Sort by
Same journal

Efficiency Enhancement in Testing Treatment Efficacy Across Multiple Populations Using Treatment Crossover Data.

Statistics in medicine·2026
Same journal

Frequentist Identification of Effective Baskets via the Generalized Information Criteria in Oncology Phase 2 Trials.

Statistics in medicine·2026
Same journal

Composite Categorical Regression for Correlated Categorical Exposures: Application to Adverse Childhood Experiences and Their Health Effects.

Statistics in medicine·2026
Same journal

LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation.

Statistics in medicine·2026
Same journal

Bayesian Bidirectional Mendelian Randomization Under Correlated and Uncorrelated Pleiotropy Using GWAS Summary Statistics.

Statistics in medicine·2026
Same journal

Transformation Discriminant Analysis for Constructing Optimal Biomarker Combinations.

Statistics in medicine·2026

Related Experiment Video

Updated: Jun 28, 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

2.1K

Statistical considerations in model-based dose finding for binary responses under model uncertainty.

Zhiwu Yan1, Min Yang2

  • 1Biostatistics Department, 89bio, Inc., San Francisco, California, USA.

Statistics in Medicine
|April 12, 2024
PubMed
Summary

This study addresses challenges in model-based dose finding for binary outcomes, proposing hybrid testing-modeling approaches. Methods include candidate model selection, optimal designs, and permutation tests for robust dose-response analysis.

Keywords:
MCP‐Moddose findingmodel uncertaintyoptimal designpermutation test

More Related Videos

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

15.7K
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

10.2K

Related Experiment Videos

Last Updated: Jun 28, 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

2.1K
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

15.7K
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

10.2K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Model-based dose finding is crucial but complex for binary responses.
  • Existing methods face challenges in model uncertainty and practical implementation.
  • Phase II dose-finding studies highlight the need for efficient statistical approaches.

Purpose of the Study:

  • To explore key design and analysis issues in hybrid testing-modeling for binary dose-response data.
  • To develop efficient statistical methodologies for model-based dose finding.
  • To address challenges in candidate model selection, optimal design, and dose-response testing.

Main Methods:

  • Considered generalized linear models for candidate model selection and specification.
  • Established D-optimal designs for efficient sample size allocation.
  • Proposed permutation-based tests for dose-response testing, avoiding normality assumptions.

Main Results:

  • Developed a framework for hybrid testing-modeling approaches for binary responses.
  • Identified optimal designs enhancing statistical efficiency in dose finding.
  • Permutation tests demonstrated robustness for dose-response analysis.

Conclusions:

  • The proposed hybrid methods offer a practical and efficient solution for model-based dose finding.
  • Optimal designs and permutation tests improve the reliability of dose-response estimation and testing.
  • This research contributes to advancing statistical methodologies in early-phase clinical trials.