Relative Effectiveness in Breast Cancer Treatment: A Health Production Approach

Abstract

Background: Pharmaceuticals’ relative effectiveness has come to the fore in the policy arena, reflecting the need to understand how relative efficacy (what can work) translates into added benefit in routine clinical use (what does work). European payers and licensing authorities assess value for money and post-launch benefit–risk profiles, and efforts to standardize assessments of relative effectiveness across the European Union (EU) are under way. However, the ways that relative effectiveness differs across EU healthcare settings are poorly understood.Methods: To understand which factors influence differences in relative effectiveness, we developed an analytical framework that treats the healthcare system as a health production function. Using evidence on breast cancer from England, Spain, and Sweden as a case study, we investigated the reasons why the relative effectiveness of a new drug might vary across healthcare systems. Evidence was identified from a literature review and national clinical guidance.Results: The review included thirteen international studies and thirty country-specific studies. Cross-country differences in population age structure, deprivation, and educational attainment were consistently associated with variation in outcomes. Screening intensity appeared to drive differences in survival, although the impact on mortality was unclear.Conclusions: The way efficacy translates into relative effectiveness across health systems is likely to be influenced by a range of complex and interrelated factors. These factors could inform government and payer policy decisions on ways to optimize relative effectiveness, and help increase understanding of the potential transferability of data on relative effectiveness from one health system to another.

Understanding Variations in Relative Effectiveness: A Health Production Approach

“…In this example, the variable for smoking would be most appropriately classified and included in the analysis as an individual level effect. In the accompanying article ( 15 ), we provide an overview of factors affecting breast cancer outcomes identified from a literature review, grouping them according to the multilevel approach: “individual level,” “provider level,” and “environment and healthcare system level” set out in Table 1 . The results are set out in Table 2 of Puig-Peiro et al ( 15 ).…”

“…We set out the elements of a multilevel model in this study. In an accompanying article ( 15 ), we use this model to categorize evidence on breast cancer to illustrate potential differences in the relative effectiveness of a new drug entering the market.…”

Model-free screening for variables with treatment interaction

“…Furthermore, we propose a thresholding procedure for variable screening. The selected variables can not only be used in decision making for ITR, but also have an impact on the relative effectiveness of treatments as pointed out in Puig-Peiro et al 39 These constitute the main results in Section 2. In Section 3, we demonstrate the effectiveness of the proposed method by simulation and show its superiority over some existing method in binary treatment case.…”