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Related Experiment Videos

Estimation of rank correlation for clustered data.

Bernard Rosner1, Robert J Glynn2

  • 1Channing Division of Network Medicine, Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, U.S.A.

Statistics in Medicine
|April 12, 2017
PubMed
Summary
This summary is machine-generated.

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This study introduces a regression-based method to estimate Pearson correlation for clustered ophthalmologic data, addressing limitations of standard estimators. The approach accurately estimates both Pearson and rank correlations in clustered samples.

Area of Science:

  • Statistics
  • Ophthalmology
  • Biostatistics

Background:

  • The sample correlation coefficient is a maximum likelihood estimator for Pearson correlation in independent bivariate normal data.
  • Ophthalmologic data often exhibits intraclass correlation in fellow eyes, violating independence assumptions.
  • Standard estimators are inadequate for clustered ophthalmologic data due to positive intraclass correlation.

Purpose of the Study:

  • To develop a regression-based method for maximum likelihood estimation of Pearson correlation (ρxy) in clustered data.
  • To extend this method for estimating partial correlation by controlling for covariates.
  • To adapt the method for estimating rank correlation in clustered datasets.

Main Methods:

  • Utilized a regression-based approach implemented with standard mixed-effects models.
Keywords:
Pearson correlationclustered datapartial correlationrank correlation

Related Experiment Videos

  • Extended the method to estimate partial correlation by adjusting for a vector of covariates (U).
  • Adapted rank correlation estimation by converting data to probit scores and estimating Pearson correlation.

    • Main Results:

    • The proposed regression-based method provides a valid maximum likelihood estimator for Pearson correlation in clustered data.
    • The method successfully extends to estimate partial correlations.
    • Rank correlation estimation for clustered data was achieved through probit transformation and Pearson correlation estimation.
    • Simulation studies supported the validity of the methods in finite samples.

    Conclusions:

    • A novel regression-based approach effectively estimates Pearson and rank correlations for clustered data, particularly relevant for ophthalmology.
    • The method is versatile, allowing for partial correlation estimation and applicable to other fields like analgesic abuse research.
    • This work provides a robust statistical framework for analyzing correlated data common in medical research.