Details
| Original language | English |
|---|---|
| Pages (from-to) | 1562-1576 |
| Number of pages | 15 |
| Journal | Statistics in medicine |
| Volume | 37 |
| Issue number | 9 |
| Publication status | Published - 6 Apr 2018 |
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.
Keywords
- correlated data, degrees of freedom, linear mixed-effects model, multiple contrast test
ASJC Scopus subject areas
- Medicine(all)
- Epidemiology
- Mathematics(all)
- Statistics and Probability
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In: Statistics in medicine, Vol. 37, No. 9, 06.04.2018, p. 1562-1576.
Research output: Contribution to journal › Article › Research › peer review
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TY - JOUR
T1 - Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models
AU - Pallmann, Philip
AU - Ritz, Christian
AU - Hothorn, Ludwig A.
PY - 2018/4/6
Y1 - 2018/4/6
N2 - 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.
AB - 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.
KW - correlated data
KW - degrees of freedom
KW - linear mixed-effects model
KW - multiple contrast test
UR - http://www.scopus.com/inward/record.url?scp=85045030872&partnerID=8YFLogxK
U2 - 10.1002/sim.7610
DO - 10.1002/sim.7610
M3 - Article
C2 - 29444546
AN - SCOPUS:85045030872
VL - 37
SP - 1562
EP - 1576
JO - Statistics in medicine
JF - Statistics in medicine
SN - 0277-6715
IS - 9
ER -