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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.
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Multiple Regression01:25

Multiple Regression

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Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
Multicompartment Models: Overview01:14

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Updated: Jun 23, 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 partially linear tree-based regression model for multivariate outcomes.

Kai Yu1, William Wheeler, Qizhai Li

  • 1Division of Cancer Epidemiology and Genetics, NCI, Rockville, Maryland 20892, USA. yuka@mail.nih.gov

Biometrics
|May 13, 2009
PubMed
Summary
This summary is machine-generated.

Analyzing multiple phenotypes jointly as a multivariate trait enhances gene discovery power for complex traits like smoking. A novel multilocus association test improves genetic association studies over univariate methods.

Related Experiment Videos

Last Updated: Jun 23, 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

Area of Science:

  • Genetics
  • Biostatistics
  • Behavioral Science

Background:

  • Complex traits, especially behavioral ones like smoking and alcoholism, involve multiple phenotypic measurements.
  • No single phenotype fully captures the complexity due to limited understanding of etiology.
  • Joint analysis of related phenotypes as a multivariate trait can increase the power to identify associated genes.

Purpose of the Study:

  • To propose a novel multilocus association test for the genetic study of multivariate traits.
  • To provide a statistical framework for evaluating associations between multivariate outcomes and genetic predictors.
  • To demonstrate the method's utility in candidate gene association studies.

Main Methods:

  • Development of a partially linear tree-based regression model for multiple outcomes.
  • The proposed method allows for joint analysis of multiple phenotypes.
  • The framework accommodates adjustment for covariates and evaluates associations with genetic markers.

Main Results:

  • Simulation studies show the proposed method maintains an acceptable type I error rate.
  • The method demonstrates improved statistical power compared to univariate analyses with multiple-comparison adjustments.
  • Application to smoking-related phenotypes illustrates its practical advantages.

Conclusions:

  • The novel multilocus association test offers a powerful approach for studying complex traits with multiple phenotypes.
  • This method enhances gene discovery by analyzing phenotypes jointly.
  • The approach is broadly applicable to assessing the joint effects of risk factors on multivariate outcomes in biomedical research.