TY - JOUR

T1 - The transition model test for serial dependence in mixed-effects models for binary data

AU - Breinegaard, Nina

AU - Rabe-Hesketh, Sophia

AU - Skrondal, Anders

N1 - © The Author(s) 2015.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests. We also propose the targeted root mean squared error of approximation (TRSMEA) as a measure of the population misfit due to serial dependence.

AB - Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests. We also propose the targeted root mean squared error of approximation (TRSMEA) as a measure of the population misfit due to serial dependence.

U2 - 10.1177/0962280215588123

DO - 10.1177/0962280215588123

M3 - Journal article

C2 - 26116615

VL - 26

SP - 1756

EP - 1773

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 4

ER -