In a longitudinal study, an individual is followed up over a period of time.Re peated measurements on the response and covariates are taken at a series of sampling times.The sampling times are often irregular and depend on covariates.A focused interest is to examine the effects of the covariates on theresponse process based on these repeated measurements.In this paper, we propose a sampling adjusted pro cedure for the estimation of the proportional mean model without having to specify the sampling model.The proposed method is robust to model misspecification of the sampling times.Large sample properties are investigated for the estimators of both regression coefficients and the baseline function.We show that the proposed estimation procedure is more efficient than the existing procedures.Large sample confidence intervals for the baseline function are also constructed by perturbing the estimation equations.A simulation study is conducted to examine the finite sam ple properties of the proposed estimators.The simulation demonstrates that the proposed method is more efficient and is robust to model misspecification of the sampling model.