Classical matched analysis, regarded as analysis of covariance (ANOCOVA) in a broad sense, makes no attempt in modeling and may therefore be inefficient. In this paper, we discuss the relative efficiencies of the ERMP (extended rank and matched-pair) test (Chen and Quade, 2000) to standard matched methods, and extend it to the case of multivariate covariables X. Taking advantage of trend information between the response Y and the covariables X by ranking after matching, ERMP test achieves better efficiency than a proposed class of weighted matched statistics. When Y is dichotomous, the optimal weighted matched statistic is equivalent to the Mantel-Haenszel statistic. Example and simulation results also suggest the conclusion.