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A semiparametric bootstrap approach to correlated data analysis problems.

Alan D Hutson1

  • 1Division of Biostatistics, University at Buffalo, Farber Hall Rm 249A, 3435 Main Street, Buffalo, NY 14214-3000, USA. ahutson@buffalo.edu

Computer Methods and Programs in Biomedicine
|February 6, 2004
PubMed
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A new bootstrap algorithm effectively handles correlated outcomes in statistical models. This method, using marginal models and Bonferroni-type correction, is accurate for mixed data types and various correlations.

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Correlated outcome variables present challenges in statistical analysis, particularly for hypothesis testing and confidence intervals.
  • Existing methods may not adequately address the complexities of mixed data types (continuous and discrete) or varying correlation structures.

Purpose of the Study:

  • To introduce a novel, user-friendly bootstrap algorithm for analyzing correlated outcome variables.
  • To provide a method that utilizes only marginal models, accommodating mixed data types and additional covariates.

Main Methods:

  • Development of a bootstrap algorithm designed for correlated outcomes.
  • Estimation of the family-wise error (FWE) rate.
  • Application of a Bonferroni-type correction to adjust for multiple comparisons or correlated outcomes.

Related Experiment Videos

Main Results:

  • The proposed bootstrap algorithm demonstrates ease of use and power.
  • The method successfully handles combinations of continuous and discrete outcome variables.
  • Simulation studies confirm the algorithm's accuracy across diverse correlation structures.

Conclusions:

  • This bootstrap approach offers a robust solution for hypothesis testing and confidence intervals with correlated outcomes.
  • The algorithm's flexibility with data types and covariates makes it broadly applicable in statistical research.
  • The method provides reliable results, as validated by simulation studies.