Description: Inputs for a decision model often come from the published literature, but may not be in a form suitable for your decision model. For example, much of the literature contains Odds Ratios and Relative Risks, which need to be transformed into probabilities in order to be used in a model. Or, the literature may contain probability estimates, but they may not be relevant for the time frame of your model. This lecture will discuss ways of deriving probabilities that are specific to your model's constraints as well as deriving probabilities from published summary statistics. This lecture is aimed at the researcher who is interested in operationalizing his or her own decision model.