This article presents a comparative analysis of three derivative-based parametric sensitivity approaches in multi-response regression estimation: marginal sensitivity, profile-based approach developed by [Sulieman, H., McLellan, P.J., Bacon, D.W., 2004, A Profile-based approach to parametric sensitivity in multiresponse regression models, Computational Statistics & Data Analysis, 45, 721-740] and the commonly used approach of the Fourier Amplitude Sensitivity Test (FAST). We apply the classical formulation of FAST in which Fourier sine coefficients are utilized as sensitivity measures. Contrary to marginal sensitivity, profile-based and FAST approaches provide sensitivity measures that account for model nonlinearity and are pertinent to linear and nonlinear regression models. However, the primary difference between FAST and profile-based sensitivity is that traditional FAST fails to account for parameter dependencies in the model system while these dependencies are considered in the analysis procedure of profile-based sensitivity through the re-estimation of the remaining model parameters conditional on the values of the parameter of interest. An example is discussed to illustrate the comparisons by applying the three sensitivity methods to a model described by set of non-linear differential equations. Some computational aspects are also explored. (C) 2009 Elsevier B.V. All rights reserved.