A real options game approach to health technology assessment

Abstract

Current economic evaluations do not explicitly acknowledge that there are multiple decision points throughout the lifecycle of new health technologies which, in the presence of uncertainty and irreversible consequences of those decisions, influence value. Real options analysis (ROA) has been proposed to overcome these limitations. However, applications to date all assumed that decisions influencing the arrival of information are made by the same actors making the decisions on adoption. The aim of this thesis is to explicitly incorporate into health technology assessment (HTA) the impact of uncertainty on decision making about new health technologies in the presence of irreversibilities. I present a series of analyses comparing “traditional” economic evaluation methods to applications of ROA using the case study of drug-eluting stents (DES). The conventional application of ROA allowed for flexibility in decisions incorporating all economic consequences of changing decisions. Over and above uncertainty surrounding the current estimate of value, three major components contributing to the economic value of the new technology were assumed to also change over time. This type of analysis can be used to determine the optimal initial decision allowing for changes in decisions and the optimal timing for review. However, it assumes that new information will always be revealed, regardless of the original decision on adoption. To reflect the combined impact of coverage, pricing and research decisions in HTA and therefore to make information arrival endogenous, a more complex approach is suggested: a Real Options Game (ROG) combining ROA with a game theoretical approach. In the ROG the HTA body and the manufacturer are assumed to play a sequential, incomplete information game, where the manufacturer has control over the arrival of information. The manufacturer decides whether to submit evidence, reduce price and conduct more research, while the HTA body decides on adoption. The DES analysis modelled a series of decision points between 2005 and 2010, with decisions not depending on hindsight, but allowing for predicted changes in value, incorporating a drift in information and responses by the other party. Payoffs were estimated for both players using a probabilistic Markov model. Optimal strategies incorporating the impact of earlier decisions on research were determined. HTA is a dynamic and interactive process, therefore results of the ROA analyses sometimes suggested a different course of action compared to traditional analyses. The best decision may depend on predictions of how other parties will react, as well as likely evolution of the evidence base and the costs of decision reversal.