Empirical-likelihood-based　inference　for　the　parameters　in　a　partially　linear　single-index　model　with　randomly　censored　data　is　investigated.　We　introduce　an　estimated　empirical　likelihood　for　the　parameters　using　a　synthetic　data　approach　and　show　that　its　limiting　distribution　is　a　mixture　of　central　chi-squared　distribution.　To　attack　this　difficulty　we　propose　an　adjusted　empirical　likelihood　to　achieve　the　standard　chi　(2)-limit.　Furthermore,　since　the　index　is　of　norm　1,　we　use　this　constraint　to　reduce　the　dimension　of　parameters,　which　increases　the　accuracy　of　the　confidence　regions.　A　simulation　study　is　carried　out　to　compare　its　finite-sample　properties　with　the　existing　method.　An　application　to　a　real　data　set　is　illustrated.