This paper outlines a decision model for evaluating the effect of information regarding toxicity, efficacy, cost and alternative treatments on anticipated adoption rates of developmental compounds. METHOD: The paper discusses the relationship between multi-attribute utility theory and people’s preferences for health outcomes. Health outcomes arising from pharmaceutical interventions are viewed as multiattribute commodities. A method for reliable quantification of health-state preferences is described. This procedure requires estimating utility weights from stated-preference (SP) data. By including health cost as an SP attribute, marginal utilities are converted to marginal dollar values to explicitly account for cost in determining market share. Additionally, SP allows for a nonlinear association between willingness-to-pay and health states reflecting the diminishing marginal utility of health. Furthermore, this technique permits estimating weights for combined health states. A recent SP design for eliciting respondent trade-offs among episode duration, symptom, daily-activity limitations, and cost is presented. Additionally, various utility specifications and simplifying assumptions are described. Finally, a rule for simulating aggregate choice behavior is presented. The prediction rule employed draws upon random utility theory. This method addresses the probabilistic nature of choices by including a residual term to represent the effect of unobserved factors on utility. A numerical example demonstrates the capability of the decision model. In this example, a general, preference-based form for health-related utility facilitates direct estimation of health attribute utility weights arising from pharmaceutical product choice. This example demonstrates how the model evaluates the impact of cost, outcomes and competition on anticipated drug adoption rates. CONCLUSION: Integrating stated-preference data, random-utility modeling, and cost-utility analysis provides a reliable way to describe preferences and predict choice behavior. This capability provides important information for developmental pharmaceutical product marketing decisions.