Case-based reasoning (CBR) and decision analysis have been two separate research areas aiming to solve problems from different perspectives. CBR is powerful to offer solutions to problems by reusing previous experiences, while decision theory exhibits its strength in dealing with uncertain, nondeterministic situations subject to likelihoods, risks, and probable consequences. In this paper, we present a novel framework of integrating CBR and decision analysis for the purpose of case-based decision analysis. CBR is employed as a methodology to reason from previous cases for building a decision model given the current situation, while decision theory is applied to the decision model learnt from previous cases to identify the most promising, Secured, and rational choices. In such a way, we take advantage of both the ability of CBR to learn without domain knowledge and the strength of decision theory to analyze under uncertainty.