Informative　dropout　often　arise　in　longitudinal　data.　In　this　paper　we　propose　a　mixture　model　in　which　the　responses　follow　a　semiparametric　varying　coefficient　random　effects　model　and　some　of　the　regression　coefficients　depend　on　the　dropout　time　in　a　non-parametric　way.　The　local　linear　version　of　the　profile-kernel　method　is　used　to　estimate　the　parameters　of　the　model.　The　proposed　estimators　are　shown　to　be　consistent　and　asymptotically　normal,　and　the　finite　performance　of　the　estimators　is　evaluated　by　numerical　simulation.