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Related Concept Videos

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...
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
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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.
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Survival Tree01:19

Survival Tree

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

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Published on: October 23, 2020

Between-within models for survival analysis.

Arvid Sjölander1, Paul Lichtenstein, Henrik Larsson

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm. arvid.sjolander@ki.se

Statistics in Medicine
|March 5, 2013
PubMed
Summary

This study introduces a new gamma between-within (BW) model for survival data, offering a more powerful analysis for clustered observational studies. The model effectively controls for confounders in twin studies, enhancing statistical power compared to traditional methods.

Keywords:
between-within modelcausal inferenceco-twin control studyconfoundingsibling comparison study

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Controlling for confounding in observational studies is crucial for valid inference.
  • Clustering individuals (e.g., twins) allows for controlling shared confounders.
  • Between-within (BW) models decompose exposure-outcome associations but are less explored for survival data.

Purpose of the Study:

  • To propose and evaluate a novel gamma Between-Within (BW) model for survival outcomes.
  • To compare the performance of the gamma BW model against stratified Cox regression.
  • To apply the gamma BW model to a real-world twin study on obesity and mortality.

Main Methods:

  • Development of a gamma Between-Within (BW) model tailored for survival analysis.
  • Comparative analysis with the standard stratified Cox regression model.
  • Application of the proposed model to twin study data investigating obesity and mortality.

Main Results:

  • The gamma BW model demonstrated increased statistical power for detecting 'within-cluster effects' compared to stratified Cox regression.
  • The proposed model exhibited robustness against potential model misspecification.
  • Identified specific scenarios where the gamma BW model might yield biased estimates.

Conclusions:

  • The gamma BW model is a promising tool for analyzing survival data in clustered observational studies.
  • It offers advantages in statistical power and robustness over traditional methods.
  • Further research is needed to address potential biases in specific situations.