Our Services

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 it 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 specialize in analyzing SEER-Medicare data, not only for solid tumors but also for lymphoma and leukemia (and eventually, myeloma). We have published our SEER Medicare work in a variety of journals, including Blood, BMC Cancer, Annals of Oncology, American Journal of Hematology, and Pharmacoeconomics.

We build budget impact models that estimate the cost from incorporating a new therapy into a health plan or system. These models are population-based, meaning that they incorporate incidence and prevalence rates for the population of interest. They generally compare a new therapy to the current standard of care, which could be one therapy or many.

Population effectiveness models are a relatively new concept, although the paradigm for constructing them has been around as long as health economics modeling. This model simulates relevant clinical outcomes for a population of patients with the disease of interest. The model incorporates many of the model-building techniques used in Markov-based and Monte Carlo-based cost-effectiveness models.

We build cost effectiveness models that assess the value of interventions. These models compare a new intervention to a standard of care with respect to two key endpoints: (1) the benefits received and (2) the costs incurred. The output is commonly expressed as the incremental cost-effectiveness ratio (ICER), which is the difference in cost divided by the difference in benefit.