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Simultaneous small‐sample comparisons in longitudinal or multi‐endpoint trials using multiple marginal models
Author(s) -
Pallmann Philip,
Ritz Christian,
Hothorn Ludwig A.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7610
Subject(s) - inference , sample size determination , marginal model , statistics , covariance , computer science , confidence interval , computation , mathematics , point estimation , econometrics , algorithm , regression analysis , artificial intelligence
Simultaneous inference in longitudinal, repeated‐measures, and multi‐endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to “marginalise” the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step. Using these estimates guarantees strong control of the family‐wise error rate, however only asymptotically. In this paper, we show how to make the approach also applicable to small‐sample data problems. Specifically, we discuss the computation of adjusted P values and simultaneous confidence bounds for comparisons of randomised treatment groups as well as for levels of a nonrandomised factor such as multiple endpoints, repeated measures, or a series of points in time or space. We illustrate the practical use of the method with a data example.