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Related Concept Videos

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
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Comparing the Survival Analysis of Two or More Groups

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Study Designs in Epidemiology01:20

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Updated: Jun 18, 2026

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

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

Jiaying Guo1,2, Mengyi Lu3, Yan Han1

  • 1Department of Biostatistics and Health Data Sciences, School of Medicine, Indiana University.

Statistics in Biopharmaceutical Research
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian adaptive umbrella trial design to find effective treatments for biomarker subgroups. The novel approach enhances treatment effect identification by adaptively clustering subgroups and borrowing information, improving trial efficiency and ethics.

Keywords:
Bayesian latent class modeladaptive designbiomarkerinformation borrowingumbrella trial

Related Experiment Videos

Last Updated: Jun 18, 2026

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

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Translational Medicine

Background:

  • Identifying effective treatments for specific patient subgroups is crucial in precision medicine.
  • Traditional clinical trial designs may lack efficiency in evaluating subgroup-specific treatment effects.
  • Adaptive designs offer flexibility but require robust statistical frameworks.

Purpose of the Study:

  • To propose a novel Bayesian adaptive umbrella trial design for identifying effective treatments in biomarker-defined subgroups.
  • To enhance the efficiency and robustness of testing subgroup-specific treatment effects.
  • To improve ethical considerations through response-adaptive randomization and early stopping rules.

Main Methods:

  • Employs a Bayesian adaptive umbrella trial design.
  • Utilizes a mixture regression model for joint analysis of binary and time-to-event outcomes.
  • Introduces a latent class model for adaptive subgroup clustering and information borrowing.
  • Incorporates response-adaptive randomization and futility/superiority stopping rules.

Main Results:

  • The proposed design effectively identifies treatments for biomarker-defined subgroups.
  • Adaptive clustering and information borrowing enhance statistical power and efficiency.
  • Simulations confirm the design's desired performance characteristics.
  • Response-adaptive randomization and early stopping rules improve trial ethics.

Conclusions:

  • The Bayesian adaptive umbrella trial design offers a robust and efficient framework for precision medicine research.
  • This design optimizes treatment allocation and decision-making in subgroup analyses.
  • The approach holds significant potential for improving clinical trial methodology and patient outcomes.