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

448
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...
448
Sampling Plans01:23

Sampling Plans

742
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
742
Censoring Survival Data01:09

Censoring Survival Data

414
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...
414
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

440
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,...
440
Stratified Sampling Method01:16

Stratified Sampling Method

14.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
14.3K
Group Design02:01

Group Design

10.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Competing-triggering effect models for multitype recurrent event data.

Biometrics·2026
Same author

Signal-reference correlation for modal structure retrieval in disordered optical fields.

Optics letters·2026
Same author

Reinnervation of Muscle Targets Enhances the Separability of Motor Unit Signals Following Peripheral Nerve Transfers.

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

Dynamic borrowing methods for basket trials with order restrictions.

Journal of biopharmaceutical statistics·2026
Same author

Correspondence.

Retina (Philadelphia, Pa.)·2026
Same author

Correspondence.

Retina (Philadelphia, Pa.)·2026

Related Experiment Video

Updated: Dec 5, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.5K

Multi-stage adaptive enrichment trial design with subgroup estimation.

Neha Joshi1, Crystal Nguyen1, Anastasia Ivanova1

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Journal of Biopharmaceutical Statistics
|October 19, 2020
PubMed
Summary

Identifying the optimal patient subgroup in clinical trials is crucial. Simpler methods outperformed complex ones for subgroup estimation, aiding treatment effect analysis.

Keywords:
Adaptive enrichmentpredictive biomarkersubgroup estimation

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Related Experiment Videos

Last Updated: Dec 5, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.5K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacoeconomics

Background:

  • Subgroup analysis is critical for personalized medicine.
  • Identifying optimal subgroups maximizes treatment benefit.
  • Existing methods for subgroup identification vary in complexity and efficacy.

Purpose of the Study:

  • To define and estimate the best subgroup in clinical trials.
  • To compare the performance of simpler methods versus complex tree-based regression for subgroup estimation.
  • To propose a robust statistical design for testing treatment effects across trial stages.

Main Methods:

  • Defined the best subgroup by maximizing a utility function balancing subgroup size and treatment effect.
  • Compared simpler subgroup estimation methods with tree-based regression approaches.
  • Developed a three-stage clinical trial design incorporating a weighted inverse normal combination test.

Main Results:

  • Simpler subgroup estimation methods demonstrated superior performance compared to complex tree-based regression for moderate effect and sample sizes.
  • The proposed three-stage design provides a framework for robust treatment effect testing.
  • The utility function effectively captured the trade-off between subgroup size and treatment effect.

Conclusions:

  • Simpler methods are often more effective for identifying optimal subgroups in clinical trials with moderate characteristics.
  • The proposed three-stage design with a weighted inverse normal combination test is a viable approach for hypothesis testing.
  • This research contributes to more efficient and effective clinical trial designs for personalized medicine.