Although it is commonly believed that the innovation of new medicines is of paramount importance for improving the health and quality of life of patients, there is also a keen recognition regarding upward-spiraling costs of innovation, drug discovery, and drug development against a backdrop of dwindling successes in research and development (R&D) efforts. We propose a new model of valuation of pharmacotherapies that attempts to secure an adequate return on investment in innovation by ensuring optimal pricing and reimbursement.

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OBJECTIVES: The objective of this workshop will be to develop skills in the use of Markov decision models to develop treatment pathways that are optimal from a pharmacoeconomic perspective. PARTICIPANTS WHO WOULD BENEFIT: Analysts or decision‐makers involved in the conduct or evaluation of pharmacoeconomic studies. Cost‐effectiveness analysis of an intervention for a chronic condition requires estimates of cost and efficacy over the rest of the patient's life time. However efficacy estimates are commonly derived from clinical trials with limited duration of follow‐up. This creates the need for modeling to estimate cost and efficacy beyond the follow‐up period. Markov modeling is the most commonly used modeling technique to perform this estimation. However, as commonly applied, Markov models are used to determine if the new intervention is cost‐effective if used instead an old one at one point in the patient's lifetime. These models do not attempt to determine the lifetime optimal treatment pathway for treating the disease. We show how Markov decision models can be used to develop an optimal treatment pathway, by assigning a treatment option to each health state so as to maximize overall net benefit. We will demonstrate how to derive the optimal treatment pathway using dynamic programming. We will present an illustrative example of the use Markov decision model for a chronic disease such as HIV. We will discuss some potential ethical issues that could be raised by the optimal policies derived from Markov decision models. We will conclude the workshop with an interactive discussion of the benefits and drawbacks of Markov decision models.

The objective of public policy decision making is to choose the set of interventions that maximize the net benefit to society. Given a set of mutually exclusive interventions, the one with the lowest cost‐effectiveness ratio is not necessarily the one that maximizes net benefit. Thus, a treatment with a higher cost‐effectiveness ratio compared to baseline may result in higher net benefits if its incremental cost‐effectiveness is less than the dollar value of the outcome. In this presentation we describe how to use cost‐effectiveness results to determine the intervention that maximizes net benefit. We also show how cost‐effectiveness results can be used to determine threshold values for the benefit than another. We then examine the effect of budget constraints on this decision making problem. We also present a graphical means of representing cost‐effectiveness results that allow for easy interpretation and use of the results. We describe a simple rule for identifying the net benefit maximizing intervention from this graph. We will illustrate the issue discussed using examples from the medical literature. This workshop should be beneficial to health care decision makers who have to interpret cost‐effectiveness results and incorporate them in their decision making process.

OBJECTIVES: The objective of this workshop will be to develop an understanding of conjoint analysis methodology, and how it can be used to conduct cost‐utility analysis and cost‐benefit analysis by capturing patient preferences. PARTICIPANTS WHO WOULD BENEFIT: Analysts involved in the conduct of pharmacoeconomic studies, particularly those interested in the patient's perspective. Cost‐utility analysis (CUA) and cost‐benefit analysis (CBA) are alternate analytical frameworks that can be used to evaluate health interventions. CUA uses a non‐monetary metric, such as quality‐adjusted life years (QALYs), to value health benefits. In CBA, both costs and benefits are measured in monetary terms. Conjoint analysis can be used to estimate the benefits of an intervention in either monetary or non‐monetary terms and can be used in both CUA and CBA. In this workshop, we present a method for conducting CUA and CBA based in conjoint analysis. Using conjoint analysis for CBA, as opposed to other methods such as contingent valuation, has the advantage of allowing a researcher to easily compute the monetary value for a range of outcomes. CUA analyses conducted using conjoint analysis allow a researcher to estimate not only overall changes in patient utility resulting from an intervention, but also changes in utility resulting from changes in any of a number of attributes of the intervention, such as mode of administration or dosing frequency. We illustrate the use of the conjoint analysis methodology to conduct CBA and CUA using data from two pilot studies. We will conclude the workshop with an interactive discussion of the relative merits of these different approaches.