Panel count data occur in many clinical and observational studies and in some situations the observation process is informative. In this article, we propose a new joint model for the analysis of panel count data with ...Panel count data occur in many clinical and observational studies and in some situations the observation process is informative. In this article, we propose a new joint model for the analysis of panel count data with time-dependent covariates and possibly in the presence of informative observation process via two latent variables. For the inference on the proposed model, a class of estimating equations is developed and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a lack-of-fit test is provided for assessing the adequacy of the model. The finite-sample behavior of the proposed methods is examined through Monte Carlo simulation studies which suggest that the proposed approach works well for practical situations. Also an illustrative example is provided.展开更多
基金partially supported by National Natural Science Foundation of China(11671267)Scientific Research Level Improvement Quota Project of Capital University of Economics and Business and Scientific Research Foundation for Young Teachers of Capital University of Economics and Business(00591654490336)+6 种基金partially supported by the National Natural Science Foundation of China(Nos.11301212,11401146)partially supported by the National Natural Science Foundation of China Grants(No.11231010,11171330 and 11021161)Key Laboratory of RCSDS,CAS(No.2008DP173182)partly supported by National Natural Science Foundation of China(11271155)Specialized Research Fund for the Doctoral Program of Higher Education(20110061110003)Scientific Research Fund of Jilin University(201100011)Jilin Province Natural Science Foundation(20101596)
文摘Panel count data occur in many clinical and observational studies and in some situations the observation process is informative. In this article, we propose a new joint model for the analysis of panel count data with time-dependent covariates and possibly in the presence of informative observation process via two latent variables. For the inference on the proposed model, a class of estimating equations is developed and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a lack-of-fit test is provided for assessing the adequacy of the model. The finite-sample behavior of the proposed methods is examined through Monte Carlo simulation studies which suggest that the proposed approach works well for practical situations. Also an illustrative example is provided.
