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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

175
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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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

251
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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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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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...
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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Survival Curves01:18

Survival Curves

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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...
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Censoring Survival Data01:09

Censoring Survival Data

196
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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Updated: Aug 24, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Statistical power and sample size calculations for time-to-event analysis.

David Zurakowski1, Steven J Staffa1

  • 1Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, Mass; Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Mass.

The Journal of Thoracic and Cardiovascular Surgery
|October 20, 2022
PubMed
Summary

This study offers a 5-step method for cardiovascular surgeons to calculate sample size and statistical power for time-to-event outcomes, ensuring research rigor and valid conclusions.

Keywords:
outcomepower analysissample sizestatisticsstudy designtime-to-event

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Area of Science:

  • Cardiovascular Surgery
  • Biostatistics
  • Clinical Research Design

Background:

  • Sample size and statistical power calculations are crucial for research integrity.
  • Adequate sample sizes ensure the validity and credibility of study findings.
  • These calculations provide readers with confidence in the study's conclusions.

Purpose of the Study:

  • To equip thoracic and cardiovascular surgeons with tools for sample size and power calculations.
  • To guide researchers in designing studies with time-to-event outcomes.
  • To enhance the quality of cardiovascular surgery research.

Main Methods:

  • A 5-step approach for sample size calculation in time-to-event studies is presented.
  • Steps include identifying outcomes, defining effect size and power, selecting statistical tests, performing calculations, and writing statements.
  • Demonstrated with 5 clinical examples using Cox regression, log-rank tests, and competing risks analysis.

Main Results:

  • The presented 5-step method facilitates accurate sample size and power calculations.
  • Clinical examples illustrate the application of the method in cardiovascular surgery.
  • The approach addresses various statistical tests relevant to time-to-event data.

Conclusions:

  • Statistical power is essential for detecting treatment effects in time-to-event studies.
  • Power and sample size justification enhance research rigor and reader assurance.
  • Valid conclusions depend on sufficient sample sizes and robust statistical planning.