Perks and Pitfalls of Performance-Linked Reimbursement for Novel Drugs: The Case of Sacubitril-Valsartan

Abstract

Background: Rising drug costs have increased interest in performance-linked reimbursement (PLR) contracts that tie payment to patient outcomes. PLR is theoretically attractive to payers interested in reducing the risk of overpaying for expensive drugs, to manufacturers working to improve early drug adoption, and to patients seeking improved access. Multiple PLR contracts were developed for sacubitril-valsartan. We evaluated how the characteristics of a PLR contract influence its performance.

Methods: We used a published cost-effectiveness model of sacubitril-valsartan. We evaluated hypothetical PLR contracts that adjusted drug payment based on observed therapy effectiveness. Ideally, these contracts reduce the uncertainty around the value obtained with purchasing sacubitril-valsartan. By reducing the financial risk in covering an ineffective therapy, PLR incentivizes insurers to increase patient access. We measured the uncertainty in value as the SD of the incremental net monetary benefit (INMB), an estimate of therapy value incorporating costs and clinical benefits. We evaluated the change in INMB SD under a variety of different assumptions regarding contract design, therapy effectiveness, and population characteristics.

Results: Over 2 years, sacubitril-valsartan led to 0.042 additional quality-adjusted life-years at an incremental cost of $4916. Using a willingness-to-pay of $150 000 per quality-adjusted life-year, this led to a mean INMB across simulations of $1416 (SD, $1720). A PLR contract that adjusted payment based on cardiovascular mortality reduced the INMB SD moderately by 20.7% while a contract based on all-cause mortality was more effective (INMB SD reduction of 27.3%). A contract based on heart failure hospitalization reduction was ineffective. PLR effectiveness increased with greater uncertainty regarding therapy effectiveness or in sicker cohorts (eg, New York Heart Association Class III/IV heart failure). Contracts required precise estimates of treatment effect in addition to trust or verifiability between manufacturers and payers concerning patient selection.

Conclusions: The development of accurate prospective estimates of treatment effectiveness using actual enrollee characteristics will be critical for successful PLR. If able to meet these requirements, PLRs could incentivize insurers to expand access to expensive treatments by reducing financial risk.

Keywords: costs and cost analysis; drug costs; economics; heart failure; prescription drugs.

Figure 1.

Idealized Performance-linked Reimbursement (PLR) This represents an idealized PLR plan across a series of simulations that represent the uncertainty of treatment performance. In Panel A, the x-axis is the incremental net monetary benefit (INMB) under conventional reimbursement with a larger value indicating higher baseline value. The y-axis is the PLR payment change with a positive number indicating increased payment to the manufacturer. With an ideal PLR plan, payment decreases with low baseline clinical benefit and poor value (low INMB with conventional payment). Payment increases with high initial value (high initial INMB). The high correlation between payment change and baseline value is displayed. Panel B is a kernel density plot of the INMB (treatment value) with conventional payment and with PLR. PLR should reduce the probability of very low and very high value.

Figure 1.

Idealized Performance-linked Reimbursement (PLR) This represents an idealized PLR plan across a series of simulations that represent the uncertainty of treatment performance. In Panel A, the x-axis is the incremental net monetary benefit (INMB) under conventional reimbursement with a larger value indicating higher baseline value. The y-axis is the PLR payment change with a positive number indicating increased payment to the manufacturer. With an ideal PLR plan, payment decreases with low baseline clinical benefit and poor value (low INMB with conventional payment). Payment increases with high initial value (high initial INMB). The high correlation between payment change and baseline value is displayed. Panel B is a kernel density plot of the INMB (treatment value) with conventional payment and with PLR. PLR should reduce the probability of very low and very high value.

Figure 2.

Change in Payments with Performance-Linked Reimbursement (PLR) and Baseline Therapy Value Abbreviations: CVD, cardiovascular death measure; HFH, heart failure hospitalization measure; PLR, performance linked reimbursement. The scatter plot displays the relationship between the INMB with conventional payment (measure of therapy value) and the payment adjustment with the given PLR contract for 10,000 simulations. The correlation is displayed for each contract. The superimposed kernel density plots are the NMBs with conventional payment (red) and PLR (green).