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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Bayesian local influence for survival models.

Joseph G Ibrahim1, Hongtu Zhu, Niansheng Tang

  • 1Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA. ibrahim@bios.unc.edu

Lifetime Data Analysis
|June 8, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian local influence method to assess how small changes in data, prior beliefs, or sampling distributions affect survival analysis models. The method helps identify influential factors and assess model sensitivity.

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Last Updated: Jun 12, 2026

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Assessing the impact of perturbations in statistical models is crucial for robust analysis.
  • Survival analysis is widely used but sensitive to data and model assumptions.

Purpose of the Study:

  • To develop a Bayesian local influence method for survival analysis.
  • To assess sensitivity to perturbations in prior distributions, sampling distributions, and individual observations.

Main Methods:

  • Introduced a perturbation model for simultaneous or individual perturbations.
  • Constructed a Bayesian perturbation manifold with geometric quantities like the metric tensor.
  • Developed local influence measures based on objective functions.

Main Results:

  • Demonstrated the method's utility through simulation studies.
  • Applied the method to two real datasets.
  • Successfully detected influential observations and characterized sensitivity to prior distributions and hazard functions.

Conclusions:

  • The proposed Bayesian local influence method provides a robust framework for sensitivity analysis in survival data.
  • This approach enhances the reliability of survival analysis by quantifying the impact of potential perturbations.