Sensitivity analysis: Could better methods be used?

Some recent articles are reviewed where sensitivity analysis (SA) is implemented via either an elementary "one factor at a time" (OAT) approach or via a derivative-based method. In these works, as customary, SA is used for mechanism identification and/or model selection. OAT and derivative based methods have important limitations: (I) Only a reduced portion of the space of the input factors is explored, (2) the possibility that factors might interact is discounted, (3) the methods do not allow self-verification, Given that all models involved are highly nonlinear and potentially nonadditive, the adopted methods might fail to provide the full effect of any given factor on the output. This could deceive the analyst, unless the analysis were really meant to focus on a narrow range around the nominal value, where linearity may be assumed. Different methods are suggested, such as a rationalized OAT screening test, a regression-based method, and two implementations of global quantitative sensitivity analysis measures. Computational cost, efficiency, and limitations of the proposed strategies are discussed, and an example is offered.