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