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Related Concept Videos

Survival Tree01:19

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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

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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.
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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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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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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...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Bivariate pseudo-observations for recurrent event analysis with terminal events.

Julie K Furberg1, Per K Andersen2, Sofie Korn3

  • 1Biostatistics GLP-1 and CV 1, Novo Nordisk A/S, Vandtårnsvej 114, Søborg, Denmark. jukf@novonordisk.com.

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Summary

This study introduces a novel bivariate marginal pseudo-observation model to simultaneously analyze recurrent and terminal events. The method effectively handles competing risks in clinical trials, offering improved interpretation of treatment effects.

Keywords:
Multi-state modelPseudo-observationsRecurrent eventsSimultaneous modelTerminal events

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Analyzing recurrent events alongside terminal events (like death) is complex in clinical trials.
  • Existing methods using intensity or marginal models may not fully capture the interplay between recurrent and terminal events.
  • Competing risks, such as death, can complicate the interpretation of treatment effects on recurrent events.

Purpose of the Study:

  • To propose a new statistical method for simultaneously modeling recurrent and terminal events.
  • To develop a marginal model that accounts for the presence of competing risks.
  • To provide a framework for hypothesis testing regarding treatment effects in the presence of both event types.

Main Methods:

  • A bivariate marginal pseudo-observation model is formulated to jointly analyze recurrent and terminal events.
  • Estimation is performed using pseudo-observations for both expected event counts and survival probabilities.
  • The approach is validated through theoretical derivations, simulation studies, and application to real-world clinical data.

Main Results:

  • The proposed bivariate model effectively handles the complexities of recurrent events in the presence of terminal events.
  • Simulation studies demonstrate the method's favorable performance compared to existing models.
  • Application to two real data examples showcases its practical utility and interpretability.

Conclusions:

  • The bivariate marginal pseudo-observation model offers a robust and interpretable approach for analyzing recurrent events with competing terminal events.
  • This method enhances the understanding of treatment effects in controlled trials where both event types are present.
  • An extension to a three-dimensional model, incorporating causes of death, is also presented, offering further analytical depth.