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Maximum likelihood estimation of marginal pairwise associations with multiple source predictors.

Liam M O'Brien1, Garrett M Fitzmaurice, Nicholas J Horton

  • 1Department of Mathematics, Colby College, 5838 Mayflower Hill, Waterville, ME 04901, USA. liam.obrien@colby.edu

Biometrical Journal. Biometrische Zeitschrift
|November 11, 2006
PubMed
Summary
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Maximum Likelihood (ML) estimation is proposed for binary outcomes, offering advantages over Generalized Estimating Equations (GEE) for analyzing pairwise predictor-outcome associations. This method provides efficient, likelihood-based inferences and handles missing data effectively.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Researchers often use multiple data sources for predictor-outcome associations.
  • Standard regression coefficients are conditional on other predictors.
  • Marginal pairwise association analysis requires different methods.

Purpose of the Study:

  • To present Maximum Likelihood (ML) estimation for binary outcomes.
  • To compare ML with Generalized Estimating Equations (GEE) for marginal associations.
  • To highlight ML benefits like efficiency and missing data handling.

Main Methods:

  • Focus on Maximum Likelihood (ML) estimation for binary outcomes.
  • Comparison with Generalized Estimating Equations (GEE) approach.
  • Exploration of asymptotic relative efficiency.

Related Experiment Videos

Main Results:

  • ML offers asymptotic efficiency and handles ignorable missing data.
  • ML allows for likelihood-based inferences on pairwise marginal relationships.
  • ML is presented as a suitable alternative to GEE for specific marginal association analyses.

Conclusions:

  • ML estimation is advantageous for analyzing marginal pairwise associations with binary outcomes.
  • ML provides a robust framework for statistical inference in such settings.
  • The study contributes to the understanding of statistical methods for multi-source predictor data.