In　this　paper,　we　propose　a　nonparametric　independence　screening　method　for　sparse　ultra-high　dimensional　generalized　varying　coefficient　models　with　longitudinal　data.　Our　methods　combine　the　ideas　of　sure　independence　screening　(SIS)　in　sparse　ultrahigh　dimensional　generalized　linear　models　and　varying　coefficient　models　with　the　marginal　generalized　estimating　equation　(GEE)　method,　called　NIS-GEE,　considering　both　the　marginal　correlation　between　response　and　covariates,　and　the　subject　correlation　for　variable　screening.　The　corresponding　iterative　algorithm　is　introduced　to　enhance　the　performance　of　the　proposed　NIS-GEE　method.　Furthermore　it　is　shown　that,　under　some　regularity　conditions,　the　proposed　NIS-GEE　method　enjoys　the　sure　screening　properties.　Simulation　studies　and　a　real　data　analysis　are　used　to　assess　the　performance　of　the　proposed　method.　(C)　2018　Elsevier　Inc.　All　rights　reserved.