In this new field, we hope to apply scientifically sound, innovative clinical study methods and practices which can contribute to more efficient
means of producing useful real world comparative evidence. In particular, we hope to apply causal modeling techniques - thus addressing the many issues of confounding found in most nonrandomized studies - to comparative effectiveness analyses. Using the wealth of existing public sector data - Medicare, Medicaid, nationally collected survey data, and emerging Part D information on pharmacotherapy, the number of comparative effectiveness
analyses that can be conducted is limitless -- the only criterion being the evidence of effectiveness of the individual therapies or interventions before comparison can ensue.