OBJECTIVES: The purpose of this presentation is to first discuss design and methodological issues associated with post-launch study design, and then to describe a framework to help identify optimal approaches to study design for post-launch studies from the methodological, operational and strategic perspective. There is increased interest in post-launch economic studies as more jurisdictions require economic data for the formal decision process of pricing and reimbursement of drugs. For example, ISPOR created a Task Force on Use of Real World Data in Coverage in Reimbursement Decisions and the AHRQ DeCIDE project produced a reference document on registries for evaluating patient outcomes to assess real-world effectiveness. Much of the data requested by agencies such as the National Institute for Health and Clinical Excellence in the UK and the Canadian Common Drug Review committees cannot be obtained before the drug is marketed. There are numerous study designs, including pragmatic clinical trials and prospective observational cohort studies (registries) for collecting health economic data (e.g. health care resource use and quality of life data) in post-launch real world studies. But what criteria should be used to decide the optimal approach? Although pragmatic clinical trials with randomization are considered the gold standard to minimize bias to estimate treatment effects, opportunities for undertaking them are limited, and from a practical perspective, they require considerable resources. Registry studies have inherent limitations of observational studies and care must be taken in their analysis and interpretation. In some instances, reimbursement agencies only require a specific piece of data, such as quality of life that can be collected in a non-comparative study design. Case examples will be used to illustrate how the framework can be applied to guide design decisions for post-launch studies.