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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...
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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...
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...
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.
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.
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

A predictive risk probability approach for microarray data with survival as an endpoint.

Dung-Tsa Chen1, Michael J Schell, James J Chen

  • 1Biostatistics Division, Moffitt Cancer Center & Research Institute, University of South Florida, Tampa, Florida 33612, USA. Dung-Tsa.Chen@moffitt.org

Journal of Biopharmaceutical Statistics
|September 11, 2008
PubMed
Summary
This summary is machine-generated.

Gene expression profiling for cancer risk classification shows variable outcomes based on the number of top genes selected. A new predictive risk probability approach averages classifications across a range of top genes, improving cancer risk assessment and identifying key genes for further study.

Related Experiment Videos

Last Updated: Jul 1, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Gene expression profiling is crucial for cancer risk classification.
  • Selecting the optimal number of top-ranked genes is critical for accurate modeling.
  • Performance can vary significantly based on the number of genes included.

Purpose of the Study:

  • To evaluate how modeling performance in cancer risk classification changes with the number of top-ranked genes.
  • To develop a robust predictive risk probability approach to account for variations in gene selection.
  • To identify a practical set of risk genes for biological validation in colon cancer.

Main Methods:

  • Genes were ranked using a univariate Cox proportional hazards model on a colon cancer dataset.
  • A predictive risk probability approach was developed, averaging classifications across a range of top k genes (k=1 to 12,500).
  • The approach was validated using univariate Cox models, survival trees, and resampling analysis.

Main Results:

  • The predictive risk probability classification showed high statistical significance (log-rank chi(2) = 110, p < 10(-16)).
  • Survival tree analysis partitioned patients into five distinct risk groups with good survival curve separation (log-rank chi(2) = 215).
  • Resampling analysis indicated that the observed variation in results was unlikely due to chance.

Conclusions:

  • The predictive risk probability approach effectively accommodates variations in gene selection for cancer risk classification.
  • This method provides a reliable risk score for patients, aiding in treatment decisions.
  • The study identified a small set of risk genes with potential for biological validation.