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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Permutation-based variance component test in generalized linear mixed model with application to multilocus genetic

Ping Zeng1,2, Yang Zhao3, Hongliang Li4

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, , Jiangsu, People's Republic of China. zpstat@xzmc.edu.cn.

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|April 22, 2015
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Summary
This summary is machine-generated.

A new permutation-based likelihood ratio test (LRT) for generalized linear mixed models (GLMM) effectively controls type I errors and enhances power for variance component testing. This method offers a simple and implementable approach for analyzing discrete data in medical studies.

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Area of Science:

  • Statistics
  • Biostatistics
  • Genetics

Background:

  • Likelihood ratio tests (LRT) are common for variance component testing in mixed effects models.
  • Generalized linear mixed models (GLMM) present computational challenges for LRT due to discrete responses and exact log-likelihood computation.
  • Existing methods for variance component testing in GLMM are limited.

Purpose of the Study:

  • To develop and evaluate a permutation-based LRT for variance component testing in GLMM.
  • To address computational difficulties in GLMM, including log-likelihood calculation and parameter estimation.
  • To compare the performance of the proposed LRT against existing methods.

Main Methods:

  • Utilized penalized quasi-likelihood algorithm for LRT statistic calculation.
  • Employed a permutation procedure to determine the null distribution of the LRT statistic.
  • Evaluated the permutation-based LRT through simulations and applied it to multilocus association analysis in case-control studies using logistic mixed effects models.

Main Results:

  • The permutation-based LRT demonstrated effective control of type I error rates.
  • The proposed LRT exhibited higher statistical power compared to score tests and mixture-based tests.
  • The LRT maintained reasonable power even when random effects deviated from normal distribution and successfully identified association signals in GAW17 data.

Conclusions:

  • Developed a permutation-based LRT for variance component testing in GLMM.
  • The proposed LRT outperforms existing methods in terms of type I error control and statistical power.
  • The method is conceptually simple, easy to implement, and effective across various scenarios.