A comparison of goodness of fit tests for the logistic GEE model.
Generalized estimating equations have become a popular regression method for analysing clustered binary data. Methods to assess the goodness of fit of the fitted models have recently been developed. However, evaluations and comparisons of these methods are limited. We discuss these methods and develop two additional statistics to evaluate goodness of fit. We evaluate the performance of each of the statistics with respect to type I error rates and power in a simulation study. Guidance is provided regarding appropriate use of the statistics under various scenarios.
Evans
Scott
S
Department of Biostatistics, Center for Biostatistics in AIDS Research, Harvard School of Public Health, 651 Huntington Avenue, Boston, MA 02115, USA. evans@sdac.harvard.edu
Li
Lingling
L
D001699:Biometry; D003627:Data Interpretation, Statistical; D003930:Diabetic Retinopathy / Q000453:epidemiology / Q000209:etiology; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D012307:Risk Factors; D014922:Wisconsin / Q000453:epidemiology