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Related Concept Videos

Survival Tree01:19

Survival Tree

79
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.
 Building a Survival Tree
Constructing a...
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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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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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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.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

122
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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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

219
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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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees.

Marjolein Fokkema1, Achim Zeileis2

  • 1Unit of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands. m.fokkema@fsw.leidenuniv.nl.

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Generalized linear mixed-effects model (GLMM) trees effectively identify subgroups with distinct growth trajectories in longitudinal data. This extended method offers improved accuracy and computational efficiency for analyzing growth curve models.

Keywords:
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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Growth curve models analyze response variable development over time.
  • Subject heterogeneity is common and often requires explanation or prediction.
  • Existing methods may lack accuracy or efficiency for complex growth patterns.

Purpose of the Study:

  • To extend generalized linear mixed-effects model (GLMM) trees for longitudinal data analysis.
  • To identify subgroups with different trajectories within linear growth curve models.
  • To assess the performance of extended GLMM trees against other partitioning methods.

Main Methods:

  • Extension of GLMM trees from clustered cross-sectional data to longitudinal data.
  • Application to linear growth curve models.
  • Performance assessment using simulated and real-world data, compared to LongCART and structural equation model (SEM) trees.

Main Results:

  • Extended GLMM trees demonstrated higher accuracy than the original algorithm and LongCART.
  • Performance was comparable to structural equation model (SEM) trees.
  • GLMM trees handle discrete and continuous time series, are robust to random-effects specification, and offer faster computation.

Conclusions:

  • Extended GLMM trees provide an accurate and efficient method for subgroup identification in growth curve analysis.
  • This approach enhances the analysis of longitudinal data with heterogeneous trajectories.
  • GLMM trees offer a flexible and computationally advantageous alternative to existing partitioning methods.