Background: Decision-analytic models in HTA today are designed and implemented by selecting a particular technique, with the state-transition (“Markov”) approach being the most popular. The various techniques appear to be quite distinct and much effort is spent accommodating the decision problem to the chosen technique. Objective: To illustrate how a novel formulation, discretely-integrated condition event (DICE) simulation, minimizes the apparent differences and allows construction of most types of models using the same concepts and structure. Methods: A DICE simulation is formulated around two core concepts: conditions and events. Conditions are aspects of the problem that persist over time while events happen at a point in time. Many conditions can exist simultaneously and they can be continuous, ordinal or nominal. Their values can change at events. The entire DICE model can be implemented in MS Excel with a simple macro in VBA to integrate the conditions at the events. Sample models showing how cohort Markov, microsimulation and discrete event simulation can be implemented in DICE will be presented as well as an approach combining aspects from multiple techniques. Additional features such as varying cycle time Markov modeling will be demonstrated as well. The advantages and limitations of DICE simulation will be discussed, emphasizing that the technique imposes few requirements. Conclusions: DICE simulation allows a modeler to focus on ensuring the model design fits the decision-analytic problem. It facilitates implementing the appropriate design without having to worry about the functional form imposed by the chosen technique.