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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,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
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...
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...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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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.
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Related Experiment Video

Updated: Jun 8, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

A supervised approach for predicting patient survival with gene expression data.

Karthik Devarajan1, Yan Zhou, Neeraj Chachra

  • 1Division of Population Science, Fox Chase Cancer Center, Philadelphia, PA 19111, karthik.devarajan@fccc.edu.

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering
|September 25, 2010
PubMed
Summary

This study introduces a new method combining partial least squares and accelerated failure time models to predict patient survival probabilities from gene expression data, even when gene numbers exceed patient numbers. The approach handles censored survival times and aids in understanding disease pathophysiology.

Related Experiment Videos

Last Updated: Jun 8, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Genomics and Bioinformatics
  • Biostatistics
  • Computational Biology

Background:

  • DNA microarrays enable simultaneous measurement of thousands of gene expression levels.
  • Understanding gene expression's role in disease pathophysiology is crucial.
  • Microarray studies with survival outcomes aim to link gene expression to time-to-event data.

Purpose of the Study:

  • To predict survival probability using gene expression data when the number of genes (p) exceeds the number of subjects (N).
  • To address the ill-conditioned problem of high-dimensional, censored survival data in microarray studies.
  • To develop a robust model for linking gene expression to survival outcomes in diseases.

Main Methods:

  • Combines partial least squares (PLS) for dimensionality reduction with the accelerated failure time (AFT) model.
  • Develops parametric methods to handle censored survival times.
  • Applies the model to cancer microarray data for illustration and pathway analysis.

Main Results:

  • The proposed model effectively predicts patient survival probabilities from high-dimensional gene expression data.
  • The methods successfully account for censored survival times.
  • Pathway analysis explored the biological relevance of identified gene expression patterns.

Conclusions:

  • The integrated PLS-AFT model offers a powerful approach for survival prediction in high-dimensional genomic studies.
  • This method enhances understanding of disease mechanisms by linking gene expression to survival.
  • The approach is validated through simulations and real-world cancer data, demonstrating its applicability and performance.