Abstract
Nonlinear mixed-effects models are very useful in analyzing repeated-measures data and have received a lot of attention in the field. It is of common interest to test for the correlation within clusters and the heterogeneity across different clusters. In this paper, we address these problems by proposing a class of score tests for the null hypothesis that all components of within- and between-subject variance are zeros in a kind of nonlinear mixed-effects model, and the asymptotic properties of the proposed tests are studied. The finite sample performance of this test is examined through simulation studies, and an illustrative example is presented.
Highlights
Repeated-measures data are frequent observations in different areas of investigation, such as economics and pharmacokinetics
For repeated-measures data and cluster data, if one can identify that there are no heterogeneity and correlation, which are caused by random effects, a simple model can be used to fit the data and the efficient statistical inference can be obtained
If there are heterogeneity and correlation among outcomes and one does not identify their existence, the overestimate or underestimate will be obtained for the parameters in model, and the precision and confidence of statistical inference are affected and even the mistaken conclusion is obtained
Summary
Repeated-measures data are frequent observations in different areas of investigation, such as economics and pharmacokinetics. For repeated-measures data and cluster data, if one can identify that there are no heterogeneity and correlation, which are caused by random effects, a simple model can be used to fit the data and the efficient statistical inference can be obtained. If there are heterogeneity and correlation among outcomes and one does not identify their existence, the overestimate or underestimate will be obtained for the parameters in model, and the precision and confidence of statistical inference are affected and even the mistaken conclusion is obtained It is an important and meaningful work to test the heteroscedasticity and correlation among outcomes in nonlinear mixed-effects model with repeated-measures data. Our aim here is to develop a class of score tests for correlation within clusters and heterogeneity across different subjects in a nonlinear mixed-effects model.
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