Objectives: Vaccinating healthy children is proposed as a strategy to produce a herd effect and protect vulnerable groups. The Hutterite Influenza Prevention Study investigated this strategy, comparing communities with or without childhood influenza immunization programs. There are costs associated with vaccination therefore there may be a trade-off between these costs and the benefits of avoiding influenza cases. This evaluation estimates the cost-effectiveness of immunizing only healthy children in preventing cases of influenza within entire communities.

Methods: Effect data and resource utilization were collected during the trial. Cost data were collected from payer, literature and Internet sources. A two-stage bootstrap (TSB) with shrinkage correction was used to estimate average costs and effects. The incremental cost effectiveness ratio (ICER) and sample uncertainty around this estimate were calculated from the TSB results.

Results: Mean costs per patient for the treatment and control arms were $69.07 and $32.66 (difference $36.41). Mean number of influenza cases for the treatment and control arms were 0.04 and 0.27 (difference 0.23). ICER was $164.12 ($28.38, $2767.75) per case of influenza averted.

Conclusions: Immunizing healthy children for influenza is more costly, yet more effective than no immunization in preventing cases in the sample. At a cost of $164.12 to prevent a case of influenza, immunizing healthy children to protect all community members may be considered costeffective. Estimated results are conservative as the influenza season was mild and the sample population was healthy. In a more severe season with a less healthy population the ICER is expected to decrease.

Objectives: Adjuvant trastuzumab is widely used in HER2-positive (HER2+) early breast cancer, and despite its cost-effectiveness, it causes substantial costs for health care. The purpose of the study was to develop a tool for estimating the budget impact of new cancer treatments. With this tool, we were able to estimate the budget impact of adjuvant trastuzumab, as well as the probability of staying within a given budget constraint.

Methods: The created model-based evaluation tool was used to explore the budget impact of trastuzumab in early breast cancer in a single Finnish hospital district with 250,000 inhabitants. The used model took into account the number of patients, HER2+ prevalence, length and cost of treatment, and the effectiveness of the therapy. Probabilistic sensitivity analysis and alternative case scenarios were performed to ensure the robustness of the results.

Results: Introduction of adjuvant trastuzumab caused substantial costs for a relatively small hospital district. In base-case analysis the 4-year net budget impact was €1.3 million. The trastuzumab acquisition costs were partially offset by the reduction in costs associated with the treatment of cancer recurrence and metastatic disease.

Conclusions: Budget impact analyses provide important information about the overall economic impact of new treatments, and thus offer complementary information to cost-effectiveness analyses. Inclusion of treatment outcomes and probabilistic sensitivity analysis provides more realistic estimates of the net budget impact. The length of trastuzumab treatment has a strong effect on the budget impact.

Objectives: In cost-minimization studies, it is important to establish noninferiority in the clinical effect of the treatments under investigation. The relationship between the proportion of patients reaching the end point in a study, equivalence limit (δ), and power is investigated in the context of cost-minimization studies with dichotomous clinical end points. Two formulations of the null-hypothesis, absolute and relative formulations of δ, will be explored.

Methods: Sensitivity analysis was performed, in which the effect of the predicted proportions and δ on the power in a noninferiority setting was investigated. The patterns found are discussed in terms of the practical relevance within the cost-minimization framework.

Results: Sensitivity analyses show different patterns of results for both null-hypotheses. The differences in these results originate from the way δ is expressed. By expressing δ as absolute difference, power grows quite fast when sample proportions are smaller than expected. In the case of a proportional δ at small sample proportions, the power to establish noninferiority remains low.

Conclusions: To obtain valid results from a cost-minimization study, care has to be taken to adapt the correct methodology for noninferiority testing in clinical outcomes. Defining δ in terms of absolute differences between treatments can lead to obscured results. Although conservative, the expression of δ as a proportion of the effectiveness of the treatment as usual is found to be closer to clinical practice. The inflated δ, resulting from smaller clinical effects than expected when absolute formulation is applied, thus can be avoided.