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Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models.

Philip Pallmann1, Christian Ritz2, Ludwig A Hothorn3

  • 1Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK.

Statistics in Medicine
|February 15, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces multiple marginal models, a novel statistical approach simplifying simultaneous inference for complex data. The method is adapted for small samples, providing adjusted P values and confidence bounds for robust analysis.

Keywords:
correlated datadegrees of freedomlinear mixed-effects modelmultiple contrast test

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs is complex.
  • Existing joint modeling approaches can be computationally intensive and difficult to implement.

Purpose of the Study:

  • To simplify simultaneous inference for complex data structures.
  • To adapt the multiple marginal models approach for small-sample data.
  • To provide adjusted P values and simultaneous confidence bounds for various comparisons.

Main Methods:

  • Introduced a novel statistical approach: multiple marginal models.
  • Marginalized the problem by fitting multiple smaller models to data subsets.
  • Estimated the overall covariance matrix in a separate step.
  • Developed methods for computing adjusted P values and simultaneous confidence bounds.

Main Results:

  • The multiple marginal models approach simplifies complex statistical inference.
  • The method is shown to be applicable to small-sample data problems.
  • Guarantees strong control of the family-wise error rate, at least asymptotically.
  • Provides practical tools for comparisons in randomized treatment groups and non-randomized factors.

Conclusions:

  • Multiple marginal models offer a powerful and accessible method for simultaneous inference.
  • The adapted approach extends the utility of multiple marginal models to small sample sizes.
  • The method facilitates robust comparisons in longitudinal, repeated-measures, and multi-endpoint studies.