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Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

655
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...
655
Cancer Survival Analysis01:21

Cancer Survival Analysis

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

Comparing the Survival Analysis of Two or More Groups

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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

321
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.
321
Survival Curves01:18

Survival Curves

554
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
554
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

486
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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Updated: Dec 22, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study.

Kyoungjune Pak1, Sae-Ock Oh2, Tae Sik Goh3

  • 1Department of Nuclear Medicine, Pusan National University Hospital, Busan, Republic of Korea.

Journal of Medical Internet Research
|May 6, 2020
PubMed
Summary
This summary is machine-generated.

ESurv is a new web tool for advanced survival analysis using multiomics data. It helps researchers easily perform complex analyses on patient survival and gene signatures.

Keywords:
The Cancer Genome Atlasgrouped variable selectionsurvival analysisuser serviceweb-based tool

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Area of Science:

  • Bioinformatics
  • Genomics
  • Biostatistics

Background:

  • Prognostic gene signatures are crucial for predicting patient survival and guiding therapeutic decisions.
  • Existing web-based survival analysis tools have limitations in handling complex, high-dimensional data.

Purpose of the Study:

  • To develop ESurv, a user-friendly web tool for advanced survival analysis.
  • To enable analysis of user-derived data and The Cancer Genome Atlas (TCGA) multiomics data.

Main Methods:

  • Survival analyses were coded in R using TCGA multiomics data.
  • Data preprocessing involved excluding insufficient patient and gene information.
  • Clinical variables were encoded using binary (0/1) and dummy variables for categorical data.

Main Results:

  • ESurv identifies prognostic gene significance via survival curves, AUC, and ROC analysis.
  • It generates prognostic variable signatures using methods like lasso and elastic net regularization.
  • Users can perform analyses on their own data and create custom gene signatures for specific cancers.

Conclusions:

  • ESurv overcomes limitations of existing tools by employing advanced statistical techniques for high-dimensional data.
  • The tool facilitates complex survival analyses for biomedical researchers, enhancing data-driven decision-making.