Context:

One of our clients wanted to assess the impact of a new diabetes medication on hospitalization rates and overall healthcare costs compared to standard treatment.

Resolution:

Our team cleaned and merged large claims datasets, mapped variables to standardized formats for HEOR analysis, applied propensity score matching to balance treatment and control groups and finally used inverse probability weighting and doubly robust estimation to adjust for confounding.

Result:

The new treatment reduced hospitalization rates as compared to standard care, the total healthcare costs were lower over a 12-month follow-up period, and the findings supported favourable reimbursement decisions and formulary inclusion.