Output from a high-order simulation model with random inputs may be difficult to fully evaluate absent an understanding of sensitivity to the inputs. We describe, and apply, a sensitivity analysis procedure to a large-scale computer simulation model of the processes associated with Nuclear Regulatory Commission (NRC) Generic Safety Issue (GSI) 191. Our GSI-191 simulation model has a number of distinguishing features: (i) The model is large in scale in that it has a high-dimensional vector of inputs; (ii) some model inputs are governed by probability distributions; (iii) a key model output is the probability of system failure-a rare event; (iv) the model's outputs require estimation by Monte Carlo sampling, including the use of variance reduction techniques; (v) it is computationally expensive to obtain precise estimates of the failure probability; (vi) we seek to propagate key uncertainties on model inputs to obtain distributional characteristics of the model's outputs; and, (vii) the overall model involves a loose coupling between a physics-based stochastic simulation sub-model and a logic-based Probabilistic Risk Assessment (PRA) sub-model via multiple initiating events. Our proposal is guided by the need to have a practical approach to sensitivity analysis for a computer simulation model with these characteristics. We use common random numbers to reduce variability and smooth output analysis; we assess differences between two model configurations; and, we properly characterize both sampling error and the effect of uncertainties on input parameters. We show selected results of studies for sensitivities to parameters used in the South Texas Project Electric Generating Station (STP) GSI-191 risk-informed resolution project.