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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Two-Part and Related Regression Models for Longitudinal Data.

V T Farewell1, D L Long2, B D M Tom1

  • 1Medical Research Council Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge CB2 0SR, United Kingdom.

Annual Review of Statistics and Its Application
|September 12, 2017
PubMed
Summary
This summary is machine-generated.

This review explores two-part statistical models for analyzing repeated measures data. It highlights key considerations for applying these mixture models to semicontinuous and zero-heavy count outcomes over time.

Keywords:
longitudinal datamarginal covariate effectsmixture distributionsrandom effectstwo-part models

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Two-part mixture distribution models are versatile for various data types.
  • Common applications include zero-inflated or hurdle models for count data and two-part models for semicontinuous data.
  • Recent focus is on applying these models to analyze repeated measures of outcomes over time.

Purpose of the Study:

  • To review the motivations for using two-part mixture models in repeated measures analysis.
  • To identify and discuss central issues and challenges associated with their application.
  • To examine specific applications for semicontinuous and zero-heavy count data, including random effects.

Main Methods:

  • Review of statistical modeling techniques for mixture distributions.
  • Analysis of two-part models for semicontinuous data.
  • Examination of models for zero-heavy count data, including hurdle and zero-inflated approaches.
  • Consideration of two-part random effects distributions for count data.

Main Results:

  • Two-part models offer a flexible framework for handling complex data structures in repeated measures.
  • Specific model choices depend on the nature of the data (semicontinuous vs. count) and the presence of excess zeros.
  • Incorporating random effects can account for within-subject correlation in longitudinal data.

Conclusions:

  • Two-part mixture models are valuable tools for analyzing longitudinal data with specific distributional characteristics.
  • Understanding the underlying data generation process is crucial for appropriate model selection.
  • Further research can refine methods for implementing and interpreting these models in complex longitudinal studies.