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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

338
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 Groups01:20

Comparing the Survival Analysis of Two or More Groups

368
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...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

223
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.
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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

361
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...
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Survival Tree01:19

Survival Tree

197
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...
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Related Experiment Video

Updated: Oct 31, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Multiobjective semisupervised learning with a right-censored endpoint adapted to the multiple imputation framework.

Lilith Faucheux1,2, Vassili Soumelis2,3, Sylvie Chevret1,4

  • 1Université de Paris, Statistic and epidemiologic research center, INSERM UMR-1153, ECSTRRA Team, Paris, France.

Biometrical Journal. Biometrische Zeitschrift
|June 28, 2021
PubMed
Summary

This study introduces a novel semisupervised learning method to classify breast cancer patients using immune parameters and survival data. The approach effectively handles missing data and identifies patterns associated with relapse-free survival.

Keywords:
consensusmultiobjective optimizationmultiple imputationsemisupervised learningsurvival endpoint

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Area of Science:

  • * Computational biology and bioinformatics
  • * Machine learning in clinical research
  • * Statistical modeling for survival analysis

Background:

  • * Semisupervised learning leverages additional knowledge for data structure analysis.
  • * Clinical applications require predictive information for data-driven classification.
  • * Identifying immune parameter patterns linked to relapse-free survival in breast cancer is crucial.

Purpose of the Study:

  • * To propose a multiobjective optimization procedure for semisupervised learning in clinical settings.
  • * To address challenges of missing data and incorporate survival time knowledge.
  • * To develop a classification method associated with a right-censored endpoint.

Main Methods:

  • * Utilized multiple imputation and consensus clustering to handle missing data.
  • * Incorporated survival information via Cox regression cross-validation error estimation.
  • * Employed multiobjective optimization to concurrently train supervised and unsupervised objectives.

Main Results:

  • * The proposed procedure outperformed an existing semisupervised method, especially in cluster number selection.
  • * Performance was robust to parameter variations on incomplete datasets, with improvements from more imputations and seeds.
  • * Performance degraded with high missing data (40%) and ambiguous data structures.

Conclusions:

  • * The developed procedure effectively constructs classifications linked to right-censored endpoints.
  • * It is applicable to potentially incomplete clinical datasets, enhancing predictive accuracy.
  • * The method demonstrates the value of integrating survival data into machine learning for cancer research.