Many Economic Evaluations of Oncotype DX May Have Misleading Conclusions
Research from the Yale School of Public Health has found that the majority of published papers analyzing the cost-effectiveness of a widely used test for breast cancer used a study design that can increase bias. These findings were published by Wang et al in the Journal of Clinical Oncology.
Oncotype DX, a gene-expression profiling test, is used in clinical care to guide chemotherapy decisions for patients with early-stage breast cancer. Several cost-effectiveness analyses (CEAs) concluded that Oncotype DX is cost-effective, yet clinical guidelines suggest Oncotype DX testing for select, rather than for all, patients.
Lead author Shi-Yi Wang, MD, PhD, Assistant Professor at Yale School of Public Health, and colleagues were interested in why literature has yielded different conclusions. In the study, the research team conducted a systematic review of Oncotype DX CEAs and applied mathematical modeling to examine the extent to which the study designs and assumptions may influence their results. They also explored whether industry funding was associated with study designs that favor Oncotype DX.
Study Findings
“Reviewing 27 analyses, we identified eight issues that might compromise the accuracy and validity of the results. We also found that industry-funded studies tended to favor Oncotype DX,” said Dr. Wang. “Our findings suggest that the majority of existing CEAs of Oncotype DX for women with early-stage breast cancer have problematic issues that may result in misleading conclusions.”
By combining systematic review with simulation modeling, Dr. Wang and his team identified factors that had large impact on CEA conclusions—for example, whether or not using prevalence information from population-based studies could lead to an opposite conclusion. An intervention with an incremental cost-effectiveness ratio less than $50,000 per quality-adjusted life year is generally viewed as cost-effective. The incremental cost-effectiveness ratio of Oncotype DX is $42,000 per quality-adjusted life year if using prevalence from a clinical trial (cost-effective), but is $167,600 per quality-adjusted life year if using population-based prevalence data (cost-ineffective).
“As precision medicine is the mainstream for future practice, assessing the relationship between costs and benefits of expensive precision medicine gene tests is important.,” said Cary Gross, MD, Professor of Medicine and Public Health at Yale and the study’s senior author. “Our results could provide critical insights for value-based frameworks, which rely on rigorous independent cost effectiveness analyses to help between high- and low-value clinical settings for specific tests.”
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
