For　the　marginal　longitudinal　generalized　linear　models　(GLMs),　we　develop　the　empirical　Cressie-Read　(ECR)　test　statistic　approach　which　has　been　proposed　for　the　independent　identically　distributed　(i.i.d.)case.　The　ECR　test　statistic　includes　empirical　likelihood　as　a　special　case.　By　adopting　this　ECR　test　statistic　approach　and　taking　into　account　the　within-subject　correlation,　the　efficiency　theory　results　of　estimation　and　testing　based　on　ECR　are　established　under　some　regularity　conditions.　Although　a　working　correlation　matrix　is　assumed,　there　is　no　need　to　estimate　the　nuisance　parameters　in　the　working　correlation　matrix　based　on　the　quadratic　inference　function　(QIF).　Therefore,　the　proposed　ECR　test　statistic　is　asymptotically　a　standard　chi(2)　limit　under　the　null　hypothesis.　It　is　shown　that　the　proposed　method　is　more　efficient　even　when　the　working　correlation　matrix　is　misspecified.　We　also　evaluate　the　finite　sample　performance　of　the　proposed　methods　via　simulation　studies　and　a　real　data　analysis.