OBJECTIVES: This workshop will describe the strategic value of including productivity measures as outcomes of concern in clinical trials, burden of illness studies, and post‐marketing research.
PARTICIPANTS WHO WOULD BENEFIT: Sponsors of clinical trials, burden of illness studies, and post‐marketing research; Employers who offer medical care benefits and other benefit programs; Outcomes researchers; Drug formulary developers; Regulators.
Until recent years, productivity has been overlooked as a measure of value in clinical trials, burden of illness studies, and post‐marketing research. However, recent advances in data development capabilities and market pressures to differentiate the plethora of drugs either under development or on the market have motivated a concern for a broader set of relevant outcome measures. In addition, concerns over the ability to document the total impact of drug therapy have emerged as important issues, as pharmaceutical developers, health care providers, employers, formulary developers, and policy‐makers strive to fully understand their own or society's return on investment in drug therapy. Recent evidence suggests that, among leading employers in the U.S., productivity‐related metrics account for more than half (53%) of the cost of employer benefit programs. In Europe, rigorously developed and supportable claims about the impact of drugs under development or already marketed can influence the drug approval process. In the U.S. and elsewhere, such evidence may influence initial and subsequent formulary decisions, and the appropriate use of alternative drug therapies. This workshop will illustrate how to 1) identify productivity‐related metrics, 2) collect and process data on these metrics, and 3) use these data in sophisticated research studies designed for clinical trials, to document the full burden of illness, or to legitimately support post‐marketing claims of the effectiveness of drug therapy. It will be shown that metrics related to morbidity, mortality and quality of life that are typical in many research studies are incomplete, and that better decisions can be made by incorporating a more complete set of relevant outcome measures.
