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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...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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An imputation approach for fitting two-part mixed effects models for longitudinal semi-continuous data.

Hyoyoung Choo-Wosoba1, Debamita Kundu1, Paul S Albert1

  • 1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, 3421National Cancer Institute, MD, USA.

Statistical Methods in Medical Research
|June 12, 2020
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Summary

This study introduces an imputation method for analyzing longitudinal data with many zeros using standard mixed models. This approach provides nearly unbiased estimation and simplifies complex analyses, making it accessible for practitioners.

Keywords:
Approximate conditional approachasymptotic biasimputationrandom effectstwo-part model

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Two-part mixed effects models are common for longitudinal data with excess zeros.
  • Existing methods often require specialized software and complex fitting procedures.
  • Correlated random effects between binary and continuous components pose analytical challenges.

Purpose of the Study:

  • To propose a novel imputation approach for two-part mixed effects models.
  • To enable the use of standard linear and generalized linear mixed models for analysis.
  • To simplify the estimation of fixed effects in complex models with many zeros.

Main Methods:

  • An approximation to the conditional distribution of positive measurements is developed.
  • This distribution is used for imputation of missing values.
  • The method allows separate fitting of standard mixed models for model components.

Main Results:

  • The proposed imputation approach yields nearly unbiased estimation across various parameter values.
  • The method is implementable using standard statistical software packages.
  • Successful illustration on longitudinal clinical trial data with numerous zero observations.

Conclusions:

  • The imputation method offers a practical and accessible alternative for analyzing longitudinal data with many zeros.
  • It effectively handles complex random effects structures.
  • Facilitates wider adoption of sophisticated statistical models in practice.