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Related Concept Videos

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

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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 Cox...
Survival Curves01:18

Survival Curves

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.
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Censoring Survival Data01:09

Censoring Survival Data

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 reasons...

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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

Sample size planning for survival prediction with focus on high-dimensional data.

Heiko Götte1, Isabella Zwiener

  • 1Merck KGaA, Darmstadt, Germany.

Statistics in Medicine
|August 4, 2012
PubMed
Summary

Planning sample size for survival prediction trials requires focusing on prediction accuracy, not statistical power. New formulas help determine training set size to minimize prediction error, accounting for variable selection bias.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Machine Learning in Healthcare

Background:

  • Traditional sample size planning prioritizes statistical power over prediction accuracy.
  • Survival prediction models are increasingly used in clinical research.
  • The impact of variable selection on prediction error and sample size is not well-established.

Purpose of the Study:

  • To present formulas for determining training set sample size for survival prediction.
  • To focus sample size determination on prediction accuracy, controlling the difference between optimal and expected prediction error.
  • To address sample size planning in the context of Cox proportional hazards models with low- and high-dimensional explanatory variables, including variable selection.

Main Methods:

  • Developed formulas for sample size determination based on prediction error control.
  • Incorporated methods for handling censoring and both low- and high-dimensional predictors.
  • Investigated variable selection using least absolute shrinkage and selection operator (LASCO) and univariable selection.
  • Validated formulas through simulations and a real data example.

Main Results:

  • Simulation results demonstrated the validity of the proposed sample size formulas.
  • The formulas effectively control the difference between optimal and expected prediction error.
  • Bias introduced by variable selection was shown to influence prediction error magnitude, impacting sample size requirements.

Conclusions:

  • Sample size planning for survival prediction should prioritize prediction accuracy.
  • The presented formulas provide a robust method for sample size determination in survival prediction models.
  • The approach is applicable to various settings, including high-dimensional data and different variable selection techniques.