Abstract
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.
| Original language | English |
|---|---|
| Journal | Statistics in Medicine |
| Volume | 37 |
| Issue number | 9 |
| Pages (from-to) | 1562-1576 |
| Number of pages | 15 |
| ISSN | 0277-6715 |
| DOIs | |
| Publication status | Published - 30 Apr 2018 |
Keywords
- Correlated data
- Degrees of freedom
- Linear mixed-effects model
- Multiple contrast test