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.