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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.
 Building a Survival Tree
Constructing a survival tree begins...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
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

Comparing the Survival Analysis of Two or More Groups

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...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Updated: May 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Adapting tree-based multiple imputation methods for multilevel data? A simulation study.

Nico Föge1,2, Jakob Schwerter3,4, Ketevan Gurtskaia3

  • 1Department of Mathematics, Otto-von-Guericke Universität Magdeburg, Magdeburg, Germany. nico.foege@ovgu.de.

Behavior Research Methods
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

New tree-based imputation methods, chained random forests and extreme gradient boosting, show promise for hierarchical data. Adapted boosting methods outperform traditional imputation for complex multilevel data, especially with higher missingness rates.

Keywords:
BiasHierarchical dataMICEMissRangerMixgbMultilevel dataMultiple imputationPowerType I error

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: May 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Statistics
  • Data Science
  • Machine Learning

Background:

  • Standard imputation methods assume data independence, limiting their use with hierarchical data.
  • Multivariate Imputation by Chained Equations (MICE) is common for hierarchical data but has limitations.
  • Tree-based methods are promising but underexplored for multilevel contexts.

Purpose of the Study:

  • To evaluate novel tree-based imputation methods adapted for multilevel data.
  • To compare their performance against traditional MICE imputation.
  • To assess performance across various cluster sizes, missingness mechanisms, and rates.

Main Methods:

  • Simulation study comparing chained random forests (missRanger) and extreme gradient boosting (mixgb) with MICE.
  • Tree-based methods adapted with cluster membership dummy variables.
  • Evaluated bias, type I error, and statistical power under random intercept and slope models.

Main Results:

  • MICE offers robust inference for level 2 variables at low missingness (10%).
  • Adapted boosting (mixgb) excels for level 1 variables at higher missingness (30%, 50%).
  • Adapted boosting surpasses MICE for level 2 variables at high missingness (50%).

Conclusions:

  • Adapted tree-based methods, particularly boosting with cluster dummies, are effective alternatives to MICE for multilevel data.
  • These methods offer improved performance, especially under higher missingness rates.
  • Appropriate adaptation is key for leveraging tree-based methods in complex data structures.