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Marginal quantile regression for dependent data with a working odds-ratio matrix.

Davide Bossoli1, Matteo Bottai2

  • 1Department of Statistics, University of Padua, Padua, Italy.

Biostatistics (Oxford, England)
|November 7, 2017
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Summary
This summary is machine-generated.

This study introduces a novel method for analyzing dependent data in quantile regression using odds ratios to model correlation. This approach offers an efficient alternative to cluster bootstrap for understanding marginal quantiles in complex datasets.

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Dependent data are common in applied research, necessitating adjustments in quantile regression.
  • Existing methods like cluster bootstrap can be inefficient and computationally intensive for large datasets.
  • Current estimating equations using Pearson's correlation are inadequate for binary variables due to probability-dependent ranges.

Purpose of the Study:

  • To propose a new method for modeling the working correlation matrix in quantile regression using odds ratios.
  • To address the limitations of Pearson's correlation for binary dependent variables.
  • To evaluate the performance of the proposed method in analyzing marginal quantiles of cognitive behavior.

Main Methods:

  • Modeling the working correlation matrix through odds ratios within logistic regression models.
  • Parametrizing correlation structures to incorporate covariates and clusters.
  • Comparing the proposed estimator's behavior to generalized estimating equations via simulations.

Main Results:

  • The proposed odds ratio-based method effectively models working correlation structures.
  • Simulations indicate the estimator performs comparably to generalized estimating equations for mean regression.
  • The method was applied to analyze marginal quantiles of cognitive behavior in an obsessive-compulsive disorder trial.

Conclusions:

  • Odds ratio modeling provides a flexible and effective approach for handling dependent data in quantile regression.
  • This method offers a viable alternative to less efficient techniques, particularly for binary outcomes.
  • The findings have implications for analyzing complex observational and trial data in various scientific fields.