To identify the problem of separating statistical noise from treatment effects in health outcomes modeling and analysis. To demonstrate the implementation of one technique, common random numbers (s), and to illustrate the value of s to assess costs and outcomes under uncertainty.
A microsimulation model was designed to evaluate osteoporosis treatment, estimating cost and utility measures for patient cohorts at high risk of osteoporosis‐related fractures. Incremental cost‐effectiveness ratios (s) were estimated using a full implementation of s, a partial implementation of s, and no s. A modification to traditional probabilistic sensitivity analysis () was used to determine how variance reduction can impact a decision maker's view of treatment efficacy and costs.
The full use of s provided a 93.6 percent reduction in variance compared to simulations not using the technique. The use of partial s provided a 5.6 percent reduction. The results using full s demonstrated a substantially tighter range of cost‐benefit outcomes for teriparatide usage than the cost‐benefits generated without the technique.
s provide substantial variance reduction for cost‐effectiveness studies. By reducing variability not associated with the treatment being evaluated, s provide a better understanding of treatment effects and risks.