Quang A. Le, PharmD, PhD

Randomized clinical trial (RCT) is the gold standard in research. While continuing to remain the most robust method to obtain quality data for efficacy and safety, RCT assumes clinical equipoise and removes the most important factor in how medical care is actually delivered; i.e., by choosing one among alternative options. A significant limitation of the traditional RCT is that strong preferences for (or against) one treatment may influence outcome and/or willingness to receive treatment. If preference is a moderator, then the treatment may be maximally effective and/or adherent for those who prefer it and minimally effective and/or adherent for those who do not. In fact, health outcomes may depend on which treatment is received (treatment effect), whether choice of treatment is given (choice effect), whether a received treatment is the preferred therapy (preference effect), and whether a specific treatment is preferred (selection effect). The current recommendations for the U.S. Value Assessment Frameworks by the International Society for Pharmacoeconomics and Outcomes Research Special Task Force emphasized that the patient perspective and preferences needed to be reflected in all discussions in assessing the value of health care. With a doubly randomized preference trial (DRPT) design, the effects of choice, preference, and selection may be separated from the effect of treatment on health outcomes; thus, it can provide important information to further improve health outcomes. This project will formalize methods to estimate treatment, choice, preference, and selection effects in DRPT design and evaluate these effects on health-related quality-of-life outcomes using data from a clinical trial using DRPT design for treatment of posttraumatic stress disorder. The findings will be important, especially in the current U.S. value-assessment frameworks, and help to enable more application of the DRPT design for assessing value of health interventions.

Challenge Award Paper: Patient-Level Modeling Approach Using Discrete-Event Simulation – A Cost-Effectiveness Study of Current Treatment Guidelines for Women with Postmenopausal Osteoporosis

In his research proposal, Dr. Le discussed discrete-event simulation (DES), an event-driven, continuous time, patient-level modeling method for health economic evaluations that addresses some limitations of other common modeling techniques. Flexibility, the ability to reflect patient heterogeneity, increased precision, and better characterization of modeling uncertainty are advantages in the DES model. Dr. Le’s proposal aims to describe and demonstrate an application of the DES model to evaluate the cost-effectiveness of the current treatment guidelines for women with postmenopausal osteoporosis.