The cost of collecting the data and assessing the benefits derived from better information about chronic granulomatous disease (CGD) are rarely examined. Bayesian decision analyses to incorporate individual risk and clinical data to identify patients for when to take the treatment. This study developed a sensitivity analysis to calculate individualized information as a function of individual outcome risk. In addition, we used simulation to explore how the ranges of numerical values for which each option will be most efficient with respect to the input parameters and decision-making thresholds. Experimental results show that, this proposed model can not only for taking action in the light of all available relevant information, but also for minimizing expected loss by considering the optimal prior / posterior decision making.