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Latent factor regression models for grouped outcomes.

D B Woodard1, T M T Love, S W Thurston

  • 1School of Operations Research and Information Engineering, Cornell University, Ithaca, New York, U.S.A.

Biometrics
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

We present regression models for correlated outcomes nested within domains, unifying random effect and factor models. This offers a spectrum of options balancing model simplicity and flexibility for analyzing complex data.

Keywords:
EpidemiologyFactor analysisMultiple outcomesRegression

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Area of Science:

  • Statistics
  • Biostatistics
  • Multivariate Data Analysis

Background:

  • Regression models are crucial for analyzing relationships between variables.
  • Handling multiple correlated outcomes, especially when nested within domains, presents unique statistical challenges.
  • Existing models may lack flexibility or parsimony when dealing with hierarchical data structures.

Purpose of the Study:

  • To develop a unified framework for regression models with multiple, domain-nested, correlated outcomes.
  • To explore the spectrum of modeling options from parsimonious random effect models to general continuous latent factor models.
  • To characterize the trade-offs between statistical parsimony and flexibility in these models.

Main Methods:

  • We frame random effect models for nested outcomes within a standard factor model structure.
  • Identifiable models are introduced along a spectrum extending existing nested random effect models.
  • Model performance and characteristics are evaluated using simulated data and real-world applications.

Main Results:

  • Random effect models for nested correlated outcomes can be integrated into a factor model framework.
  • A spectrum of models is proposed, offering varying degrees of parsimony and flexibility.
  • Application to sexually dimorphic traits in male infants demonstrates the models' utility.

Conclusions:

  • The proposed framework provides a flexible approach to modeling multiple correlated outcomes nested in domains.
  • Understanding the spectrum of models aids in selecting appropriate methods based on data complexity and research questions.
  • These models offer valuable tools for analyzing complex biological and health-related data, such as developmental traits.