Payer perceptions on the use of economic models in oncology decision making

BACKGROUND: To support oncology formulary decisions, especially with accelerated regulatory approvals and niche populations, payers desire data beyond what regulators review. Economic models showing financial impact of treatments may help, but data on payers’ use of economic models in oncology are limited. OBJECTIVE: To assess payer perceptions regarding use of economic models in informing oncology formulary decisions. METHODS: A multidisciplinary steering committee involving health economists and payers developed a survey containing singleanswer, multiple-answer, and free-response questions. The pilot survey was tested at a mini-advisory board with 5 US payers and revised based on feedback. In February 2020, the survey was distributed to 221 US payers through the AMCP Market Insights program and 10 additional payer panelists, who were invited to discuss survey results. Results were presented primarily as frequencies of responses and evaluated by plan size, type of health plan, and geography (regional vs national). Differences in categorical data responses were compared using Pearson chi-square or Fisher’s exact tests. Two-tailed values were reported and an alpha level of 0.05 or less was used to indicate statistical significance. RESULTS: Overall, 106 of 231 payers completed the survey (45.9%); 45.5% represented small plans (< 1 million lives), and 54.5% represented large plans (≥ 1 million lives). Respondents were largely pharmacists (89.9%), and 55.6% indicated that their job was pharmacy administrator. Payers indicated moderate/most interest in cost-effectiveness models (CEMs; 85.3%) and budget impact models (BIMs; 80.4%). Overall, 51.6% of respondents claimed oncology expertise on their pharmacy and therapeutics committees. Large plans were more likely to have expertise in reviewing oncology economic models than small plans (55.6% vs 31.1%, P = 0.015). The most common reasons for not reviewing economic models included “not available at time of review” (44.1%) and “potential bias” (38.2%). Overall, 43.1% of payers conduct analyses using their own data after reviewing a manufacturer-sponsored economic model. To inform formulary decisions, 62.7% of payers use BIMs and 66.7% use CEMs sometimes, often, or always. When comparing therapies with similar safety/efficacy profiles, 68.6% of payers reported economic models as helpful a moderate amount, a lot, or a great deal. Over one-third of payers (37.3%) were willing to partner with manufacturers on economic models using their plans’ data. Payers valued preapproval information, data on total cost of care, and early access to models. Concerns remained regarding model transparency and assumptions. CONCLUSIONS: Most US payers reported interest in using economic models to inform oncology formulary decision making. Opportunities exist to educate payers in assessing economic models, especially among small health plans. Ensuring model availability at launch, transparency in model assumptions, and payer-manufacturer partnership in model development may increase the utility of oncology economic models among US payers.