Analyses of Observational Data Marc Halperin
Analyses of Observational Data
For every disease state, understanding what happens to patients over time is critical to communicating the value of a product for treating a disease (or its complications). For example, event rates are used in building models, treatment effects can be estimated in populations that were not included in clinical trials, and long-term costs of care can be estimated and compared against models for validation purposes. In particular, these kinds of analyses are critical for conducting comparative effectiveness research.
We have worked in many disease areas to analyze exposures and outcomes, and we have particular experience in oncology and nephrology. We work with federally funded data like the SEER-Medicare data, the United States Renal Data System (USRDS), and the Nationwide Inpatient Sample (NIS) and other HCUP datasets. We also work with proprietary datasets from health plans and providers, and we can license specific data sets for our projects, as appropriate.