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Related Concept Videos

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.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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 until a...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
Comparing the Survival Analysis of Two or More Groups01:20

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...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
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.
 Building a Survival Tree
Constructing a survival tree begins...

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Updated: Jun 13, 2026

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

Basic concepts and methods for joint models of longitudinal and survival data.

Joseph G Ibrahim1, Haitao Chu, Liddy M Chen

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. ibrahim@bios.unc.edu

Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
|May 5, 2010
PubMed
Summary
This summary is machine-generated.

Joint models integrate longitudinal biomarker data with survival outcomes, crucial for cancer clinical trials. This approach enhances understanding of disease progression and treatment effectiveness for improved patient survival analysis.

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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

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Last Updated: Jun 13, 2026

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

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

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Cancer Research

Background:

  • Longitudinal biomarkers (e.g., tumor cells, immune response, quality of life) are often associated with survival endpoints in cancer studies.
  • Joint models are essential for analyzing complex relationships between such biomarkers and time-to-event outcomes (relapse-free, overall survival).

Purpose of the Study:

  • To provide an introductory overview of joint models for longitudinal and survival data.
  • To discuss key issues in the design and analysis of clinical trials employing joint models.
  • To illustrate joint modeling applications using a real-world cancer trial example.

Main Methods:

  • Overview of joint modeling principles for longitudinal and survival data.
  • Discussion of design and analysis considerations for clinical trials using joint models.
  • Application of joint models to data from the Eastern Cooperative Oncology Group (ECOG) trial E1193.
  • Simulation studies to evaluate the operating characteristics of joint models.

Main Results:

  • Demonstration of joint model application in analyzing biomarker data and survival outcomes from ECOG E1193.
  • Insights into the performance and behavior of joint models under various scenarios through simulations.

Conclusions:

  • Joint models offer a robust framework for integrating longitudinal biomarker data with survival analysis in cancer clinical trials.
  • Understanding the design and analysis issues is critical for effective implementation of joint models.
  • The presented analysis and simulations highlight the utility and characteristics of joint models for cancer research.