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Simultaneous Inference Using Multiple Marginal Models.

Ludwig A Hothorn1, Christian Ritz2, Frank Schaarschmidt3

  • 1Leibniz University Hannover, Hannover, Germany.

Pharmaceutical Statistics
|August 21, 2024
PubMed
Summary
This summary is machine-generated.

This tutorial introduces simultaneous inference for low-dimensional data, offering adjusted p-values and confidence intervals beyond mean comparisons. It leverages correlation

Keywords:
CRAN packagesmaxT‐testmultiple marginal modelssimultaneous inference

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Area of Science:

  • Biostatistics
  • Statistical Inference
  • Multivariate Data Analysis

Background:

  • Simultaneous inference is crucial for multiple comparisons in statistical analysis.
  • Existing methods often lack adjusted p-values and confidence intervals for complex endpoint structures.
  • The influence of correlation on statistical tests needs careful consideration.

Purpose of the Study:

  • To describe a single-step method for low-dimensional simultaneous inference.
  • To provide adjusted p-values and confidence intervals for various comparisons.
  • To demonstrate the application of the multiple marginal models (mmm) approach.

Main Methods:

  • Utilizing the influence of correlation on multivariate t-distribution quantiles.
  • Estimating the correlation matrix via the multiple marginal models (mmm) approach.
  • Employing the maxT-test with mmm in real data scenarios using R packages.

Main Results:

  • The method supports analysis of different-scaled, correlated multiple endpoints.
  • It enables joint analysis of correlated binary endpoints.
  • Applications include modeling dose, joint testing of dose/time, subgroups, and various regression models.

Conclusions:

  • The described simultaneous inference method is versatile and applicable to complex data structures.
  • It offers robust statistical inference for multiple correlated endpoints.
  • The multiple marginal models approach provides a flexible framework for advanced statistical analyses.