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

200
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...
200
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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...
12.1K
Aggregates Classification01:29

Aggregates Classification

328
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
328
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Cancer Survival Analysis01:21

Cancer Survival Analysis

357
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
357

You might also read

Related Articles

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

Sort by
Same author

A Bayesian approach towards the identification of latent subgroups.

Statistical methods in medical research·2025
Same author

2-in-1 adaptive design with dose optimization accounting for ranked dose response.

Contemporary clinical trials·2025
Same author

Creating a Proxy for Baseline Eastern Cooperative Oncology Group Performance Status in Electronic Health Records for Comparative Effectiveness Research in Advanced Non-Small Cell Lung Cancer.

JCO clinical cancer informatics·2025
Same author

Association of Tumor Mutational Burden and PD-L1 with the Efficacy of Pembrolizumab with or without Chemotherapy versus Chemotherapy in Advanced Urothelial Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2024
Same author

Qualification of a 21-valent pneumococcal urine antigen detection assay and development of clinical positivity cutoffs.

Bioanalysis·2024
Same author

Contribution of tumour and immune cells to PD-L1 expression as a predictive biomarker in metastatic triple-negative breast cancer: exploratory analysis from KEYNOTE-119.

The journal of pathology. Clinical research·2024
Same journal

Correction.

Journal of biopharmaceutical statistics·2026
Same journal

Leveraging external controls in clinical trials: estimands, estimation, assumptions.

Journal of biopharmaceutical statistics·2026
Same journal

Special issue of nonclinical statistics in regulatory applications guest editors' notes.

Journal of biopharmaceutical statistics·2026
Same journal

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.

Journal of biopharmaceutical statistics·2026
Same journal

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.

Journal of biopharmaceutical statistics·2026
Same journal

Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous outcomes.

Journal of biopharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Covariate-adjusted value-guided subgroup identification via boosting.

Jinchun Zhang1, Pingye Zhang2, Junshui Ma1

  • 1Merck & Co. MRL, BARDS, Rahway, New Jersey, USA.

Journal of Biopharmaceutical Statistics
|November 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces CAVboost, a new method for identifying patient subgroups that benefit most from treatment. CAVboost improves personalized therapy by accounting for prognostic factors, enhancing subgroup identification for various outcomes.

Keywords:
Precision medicinegradient tree boostingindividual treatment rulesubgroup identification

More Related Videos

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
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

Related Experiment Videos

Last Updated: Jul 11, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
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
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Personalized Medicine

Background:

  • Treatment effects often vary across patient subgroups, necessitating subgroup analysis for personalized therapies.
  • Existing statistical methods for subgroup identification are continuously evolving.
  • A recent advancement is value-guided subgroup identification, maximizing subgroup-level treatment benefit for survival outcomes.

Purpose of the Study:

  • To extend the value-guided subgroup identification framework to continuous and binary outcomes.
  • To introduce Covariate-Adjusted Value-guided subgroup identification via boosting (CAVboost) to account for prognostic effects.
  • To improve the power of subgroup identification by isolating treatment effects.

Main Methods:

  • Applied the value-guided framework to continuous and binary outcomes.
  • Developed CAVboost by incorporating covariate-adjusted treatment effect estimation.
  • CAVboost utilizes covariates to adjust for prognostic effects, thereby isolating treatment effects for subgroup analysis.

Main Results:

  • The proposed CAVboost framework was successfully applied to continuous and binary outcomes.
  • CAVboost demonstrated improved capability in identifying relevant patient subgroups.
  • The method effectively accounts for prognostic effects, enhancing treatment effect detection.

Conclusions:

  • CAVboost offers an effective approach for subgroup identification by adjusting for prognostic factors.
  • This method enhances the ability to detect treatment effects across subgroups for continuous and binary outcomes.
  • CAVboost contributes to the development of more precise personalized therapies.