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

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

N1 - CURIS 2018 NEXS 070

PY - 2018/4/30

Y1 - 2018/4/30

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

U2 - 10.1002/sim.7610

DO - 10.1002/sim.7610

M3 - Journal article

C2 - 29444546

SN - 0277-6715

VL - 37

SP - 1562

EP - 1576

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 9

ER -