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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...
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Modeling adverse birth outcomes via confirmatory factor quantile regression.

Lane F Burgette1, Jerome P Reiter

  • 1Department of Statistical Science, Duke University, Durham, North Carolina 27708, USA. lb131@stat.duke.edu

Biometrics
|June 22, 2011
PubMed
Summary

This study introduces a Bayesian quantile regression model incorporating latent factors, suitable for analyzing complex relationships in birth weight data. The model helps understand how psychosocial health and tobacco use impact lower birth weight quantiles.

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Area of Science:

  • Biostatistics
  • Econometrics
  • Psychometrics

Background:

  • Traditional regression models may not fully capture the influence of unobserved factors on response distributions.
  • Quantile regression is valuable for examining predictors' effects across the entire response spectrum, particularly in the tails.
  • Latent variables, representing underlying constructs, often play a significant role in observed outcomes but are challenging to model directly.

Purpose of the Study:

  • To present a novel Bayesian quantile regression model that integrates latent factors via a confirmatory factor structure.
  • To demonstrate the model's utility in scenarios where covariates are indicators of underlying scientific constructs.
  • To investigate the impact of latent psychosocial health and tobacco usage on lower birth weight quantiles.

Main Methods:

  • Development of a Bayesian quantile regression framework.
  • Incorporation of a confirmatory factor analysis structure within the model's design matrix.
  • Application to birth weight data, modeling latent variables for psychosocial health and tobacco use.
  • Utilizing the specialized R package 'factorQR' for model fitting.

Main Results:

  • The proposed model effectively estimates the influence of latent factors on specific quantiles of the birth weight distribution.
  • Significant associations were found between latent psychosocial health, tobacco usage, and lower birth weight quantiles.
  • The model provides a robust approach for handling measurement error and unobserved heterogeneity.

Conclusions:

  • The Bayesian quantile regression model with latent factors offers a powerful tool for analyzing complex predictor-response relationships.
  • This approach enhances the understanding of factors influencing extreme outcomes, such as low birth weight.
  • The 'factorQR' R package facilitates the application of this advanced statistical methodology.