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Median regression for longitudinal data.

Xuming He1, Bo Fu, Wing K Fung

  • 1Department of Statistics, University of Illinois, Champaign, Illinois 61820, USA.

Statistics in Medicine
|December 4, 2003
PubMed
Summary
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We compared three median regression estimators for longitudinal data. The independence assumption estimator is efficient and simple, especially with time-invariant covariates or low within-subject correlation.

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Longitudinal data analysis requires robust statistical methods.
  • Median regression offers an alternative to mean regression, particularly for non-normally distributed outcomes.
  • Estimating median regression with correlated longitudinal data presents unique challenges.

Purpose of the Study:

  • To review and compare three distinct estimators for median regression in linear models with longitudinal data.
  • To evaluate the performance of these estimators under various conditions.

Main Methods:

  • The study employs asymptotic efficiency calculations.
  • Finite-sample Monte Carlo simulations are utilized for performance assessment.
  • Estimators are based on weighting, decorrelating, and the working assumption of independence.

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Main Results:

  • The relative performance of the estimators is dependent on the nature of the covariates.
  • The independence assumption estimator demonstrates computational simplicity.
  • This estimator exhibits good performance with time-invariant covariates or small within-subject correlations.

Conclusions:

  • The independence assumption estimator is a practical choice for specific longitudinal data scenarios.
  • Its finite-sample performance is favorable, often exceeding asymptotic predictions.
  • The choice of estimator should consider covariate characteristics and data correlation structure.