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Related Concept Videos

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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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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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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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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Hazard Rate01:11

Hazard Rate

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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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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.
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Related Experiment Video

Updated: Jun 24, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Familywise error for multiple time-to-event endpoints in a group sequential design.

Henrik F Thomsen1, Nanna L Lausvig2, Christian B Pipper2,3

  • 1Department of Biostatistics, Novo Nordisk A/S, Aalborg, Denmark.

Statistics in Medicine
|June 9, 2024
PubMed
Summary

This study examines familywise error rate (FWER) in group sequential designs for time-to-event data. A simulation-based method is proposed to better control FWER for secondary endpoints compared to alpha-spending approaches.

Keywords:
familywise error rategroup sequential designsecondary endpointstime to event endpoints

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Group sequential designs are common in clinical trials for early efficacy stopping.
  • Hierarchical testing is used for secondary endpoints to manage multiplicity.
  • Accurate control of familywise error rate (FWER) is crucial for valid inferences.

Purpose of the Study:

  • To investigate the familywise error rate (FWER) for time-to-event endpoints in group sequential designs with hierarchical testing.
  • To identify factors influencing the correlation between log-rank test statistics in such designs.
  • To propose an improved method for assessing FWER and selecting critical values for secondary endpoints.

Main Methods:

  • Theoretical derivations and Monte Carlo simulations were employed.
  • The study analyzed the correlation between log-rank test statistics at interim and final analyses.
  • A simulation-based method for FWER assessment was developed and compared to alpha-spending methods.

Main Results:

  • The correlation between log-rank test statistics is not congruent with canonical correlations for normal endpoints.
  • Correlation is dependent on censoring levels, endpoint hazard rates, and hazard ratio.
  • The proposed simulation-based method offers better FWER control than alpha-spending for secondary endpoints.

Conclusions:

  • Standard assumptions for correlation may not hold for time-to-event data in group sequential trials.
  • A simulation-based approach is recommended for accurate FWER assessment and critical value selection in complex hierarchical testing scenarios.
  • This method optimizes operating characteristics for clinical trial designs with multiple time-to-event endpoints.