Robust Small Sample Inference for Generalised Estimating Equations: An Application of the Anova-type Test

Asymptotically, the Wald‐type test for generalised estimating equations (GEE) models can control the type I error rate at the nominal level. However in small sample studies, it may lead to inflated type I error rates. Even with currently available small sample corrections for the GEE Wald‐type test, the type I error rate inflation is still serious when the tested contrast is multidimensional. This paper extends the ANOVA‐type test for heteroscedastic factorial designs to GEE and shows that the proposed ANOVA‐type test can also control the type I error rate at the nominal level in small sample studies while still maintaining robustness with respect to mis‐specification of the working correlation matrix. Differences of inference between the Wald‐type test and the proposed test are observed in a two‐way repeated measures ANOVA model for a diet‐induced obesity study and a two‐way repeated measures logistic regression for a collagen‐induced arthritis study. Simulation studies confirm that the proposed test has better control of the type I error rate than the Wald‐type test in small sample repeated measures models. Additional simulation studies further show that the proposed test can even achieve larger power than the Wald‐type test in some cases of the large sample repeated measures ANOVA models that were investigated.