Pricing methods in outcome-based contracting: integration analysis of the six dimensions (6 δs)

Abstract

Aims

Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The six dimensions have been described separately: (δ1) cost-effectiveness analysis and cost-utility analysis-based pricing; (δ2) willingness-to-pay-based pricing; (δ3) reference-based pricing; (δ4) safety-based pricing; (δ5) risk of efficacy failure-based pricing; and (δ6) adherence-based pricing. The final step is to integrate the various dimension-specific pricing estimates into a composite estimate termed the All-Dimensional Price (ADP). We describe the methodology for this integration and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib.

Materials and methods

For better accuracy in estimating the ADP, we used the prices generated from the six dimensions at scenario levels, not at the dimension-specific price (DSP) level. We pooled the price estimates and performed Monte Carlo Simulations (MCS) for the price scenarios generated by the six dimensions. We used the results of the proof-of-concept exercise involving osimertinib in NSCLC with EGFR mutation to estimate the ADP in two hypothetical contracts: 1-year (2019–2020) and 2-year contract (2019–2021).

Results

The average of the 30-day prescription estimates from the six dimensions averaged $10,819 (SD=$8,486) for the 1-year contract and $10,730 (SD=$8,500) for the 2-year contract. MCS yielded for the 1-year contract an ADP of $10,959 (or −25.02% the 2018 WAC price) and an ADP for the 2-year contract was $10,788 (or −26.19% the 2018 WAC price).

Conclusions

We demonstrated that the integration of the prices from the six dimensions of the Six Delta platform and market conditions is feasible and yields multidimensional prices estimates to support outcome-based pricing/contracting.

Keywords:

• Six delta platform
• outcome-based-contracting
• pricing methods
• osimertinib
• lung cancer

JEL classification codes:

• D40
• C00

View correction statement:

The six Delta platform for outcome-based contracting for pharmaceuticals

This article is part of a series including:

Pricing methods in outcome-based contracting: δ1: cost effectiveness analysis and cost-utility analysis-based pricing

Introduction

Building on the papers that proposed the six dimensions of the six delta (6 δ’s) platform for pricing drugs under outcome-based contracting, we present here the methodology for integrating the six dimension-specific prices (DSPs) into the all-dimensional price (ADP). The DSPs were derived from six perspectives on, and approaches to, outcome-based pricing: 1) cost-effectiveness analysis and cost-utility analysis-based pricing¹; 2) willingness-to-pay-based pricing²; 3) reference-based pricing³; 4) safety-based pricing⁴; 5) risk of efficacy failure-based pricing⁵; 6) adherence-based pricing⁶. Each dimension has a specific methodology to generate a single price point at the dimensional level (DSP) and we complemented the technical description of each dimension with a proof-of-concept analysis of osimertinib in the treatment of NSCLC with EGFR mutation for two hypothetical time-dependent outcome-based contracts: 1-year contract (2019–2020) and 2-year contract (2019–2021).

The ADP is a more informative and balanced price estimate than each of the DSPs as it represents the average of all price variations within and among the dimensions. Inherently important in this is the assumption of random pattern of price variation, a methodology applied in cases when there is imperfect knowledge about a product^7,8. Under this assumption, different prices for a product are suggested under varying degrees of uncertainty, until less uncertain information becomes available and better inform pricing. Then, our six-delta platform aims to reduce the random pattern of price variation by contributing outcomes-driven probabilistic approaches to price estimation.

Methods

Overview

As shown in Figure 1, a price pool is established for price scenarios generated from the six dimensions (not the DSPs) and this is for better prediction. The average and standard deviation are calculated. From this, a gamma distribution, scaled from 0 to positive infinity (0, +∞), is specified as per Equations (1)(1) $$ɑ\mathrm{ }=\mathrm{ }\left({\overline{X}}^2\right)/\left({\text{SD}}^2\right)$$(1) and (2)(2) $$\beta \mathrm{ }=\mathrm{ }\left(S{D}^2/\overline{X}\right)$$(2) ⁹. (1) $$ɑ\mathrm{ }=\mathrm{ }\left({\overline{X}}^2\right)/\left({\text{SD}}^2\right)$$(1) (2) $$\beta \mathrm{ }=\mathrm{ }\left(S{D}^2/\overline{X}\right)$$(2) where ɑ is shape; β is scale; X̄ is average of all price scenarios; SD is standard deviation.

Figure 1. Outcome-based price assessment framework. *DSPAdherence is subject to change based on real-world effectiveness results. In the framework, δ1 and δ2 were based on long-term assessment; δ3–δ6 were based on short-term assessment. Abbreviations. DSP, dimension-specific price; ADP, average of all dimensional prices integrated and simulated by Monte Carlo Simulation.

Gamma distribution was used because it fits monetary estimates. It is bounded between 0 to +∞ with positive skewness to the right. There are two technical issues to consider when applying Equations (1)(2) $$\beta \mathrm{ }=\mathrm{ }\left(S{D}^2/\overline{X}\right)$$(2) and (2)(2) $$\beta \mathrm{ }=\mathrm{ }\left(S{D}^2/\overline{X}\right)$$(2) to estimate the ADP for specific contract. First, to more accurately estimate the ADP, the average price should use the prices for each dimension at the level scenarios developed for a given dimension; that is, the prices generated in each scenario within each dimension. Second, adjustments for inflation are not required when estimating the ADP because such adjustments were already imputed at the dimensional level.

Proof-of-concept: application to osimertinib in NSCLC with EGFR mutation

Background

The papers presenting the methods for the six dimension-specific prices included proof-of-concept analyses for osimertinib^1–6, a third-generation, irreversible EGFR tyrosine kinase inhibitor (TKI) approved in 2018 for the treatment of NSCLC with EGFR mutation based on the results of the FLAURA clinical trial¹⁰. At 2018, a wholesale acquisition cost (WAC) of osimertinib was $14,616. Its price is approximately double that of the first- and second-generation EGFR-TKIs gefitinib, erlotinib, and afatinib¹¹. Hence, we integrate the previously updated DSPs to derive a proposed ADP for two hypothetical contracts: 1-year contract (2019–2020) and 2-year contract (2019–2021).

Model inputs

Table 1 summarizes the price estimates for each dimension for each of the 6 DSPs. For purpose of information, Table 2 presents the 6 DSPs, comparing these to the WAC of $14,616 for a 30-day prescription of osimertinib shows the variation in pricing generated by the six methods for outcome-based pricing. However, for the integration exercise, we used the scenario estimates within each of the six methods.

Table 1. Inputs integrated into the six-delta platform.

Dimensional price scenarios	1-year contract (2019–2020)	2-year contract (2019–2021)
Dimension 1. Cost-effectiveness analysis and cost utility analysis-based pricing^1
Certainty of cost-effectiveness analysis (CEA)
0% certainty of CEA	$0	$0
50% certainty of CEA	$14,680	$14,721
100% certainty of CEA	$41,666	$41,782
Certainty of cost-utility analysis (CUA)
0% certainty of CUA	$0	$0
50% certainty of CUA	$14,680	$14,721
100% certainty of CUA	$28,072	$28,150
Dimension 2. Willingness-to-pay-based pricing^2
<1xGDP
Monopolistic market	$0	$0
Competitive market	$0	$0
1xGDP
Monopolistic market	$0	$0
Competitive market	$0	$0
3xGDP
Monopolistic market	$5,281	$5,864
Competitive market	$2,592	$2,890
>3xGDP
Monopolistic market	$14,680	$14,721
Competitive market	$14,680	$14,721
Dimension 3. Reference-based pricing^3
Based on PPP (UK, CAN)	$10,827 $5,595	$10,867 $5,636
Based on real GDP per capita (UK, CAN)	$10,095 $9,115	$10,136 $9,156
Based on percentage (%) of GDP spent on pharmaceuticals (UK, CAN)	$13,712 $7,647	$13,753 $7,688
Based on minimum foreign price	$6,944	$6,963
Based on average foreign prices	$7,201	$7,221
Dimension 4. Safety-based pricing (mean, lower, upper)^4	$14,627 $14,608 $14,642	$14,520 $14,453 $14,578
Dimension 5. Risk of efficacy failure-based pricing^5
Risk of OS failure-based pricing (lower, upper)	$9,170, $14,680	$9,489, $14,721
Risk of PFS failure-based pricing (lower, upper)	$11,969 $14,680	$13,259 $14,721
Dimension 6. Adherence-based pricing^6
Based on OS (lower upper)	$13,835 $14,680	$12,054 $14,721
Based on PFS (lower, upper)	$11,997 $14,680	$8,724, $14,721

Abbreviations. CEA, cost-effectiveness analysis; CUA, cost-utility analysis; UK, United Kingdom; CAN, Canada; PPP, purchasing power parity; GDP, gross domestic product; OS, overall survival; PFS, progression-free survival.

Table 2. Results for the 6 DSPs estimates and the ADP estimate^a.

Dimensions	1-year contract (2019–2020)^b	2-year contract (2019–2021)^c	Reference
Dimension 1. Cost-effectiveness and cost-utility analysis-based pricing
DSPCEA/CUA (price adjustment %)	$16,391 (+12.14%)	$16,677 (+14.10%)	1
Dimension 2. Willingness-to-pay-based pricing
DSPWTP (price adjustment %)	$4,547 (−68.89%)	$4,704 (−67.82%)	2
Dimension 3. Reference-based pricing
DSPReference (price adjustment %)	$9,395 (−35.72%)	$9,442 (−35.40%)	3
Dimension 4. Safety-based pricing
DSPSafety (price adjustment %)	$14,627 (+0.08%)	$14,516 (−0.68%)	4
Dimension 5. Risk of efficacy failure-based pricing
DSPRisk of efficacy failure (price adjustment %)	$12,652 (−13.44%)	$13.019 (−10.93%)	5
Dimension 6. Adherence-based pricing
DSPAdherence (price adjustment %)	$13,785 (−5.69%)	$12,582 (−13.92%)	6
All dimensions integrated
All dimensions integrated-based average price (SD)	$10,819 ($8,486)	$10,730 ($8,500)
ADP (price adjustment %)	$10,959 (−25.02%)	$10,788 (−26.19%)

^aPrice adjustment of 2018 WAC price for 30-day prescription of osimertinib.

^bInflation rate in 2019–2020 was projected at +0.4395833% but rounded to +0.44% for reporting.

^cInflation rate in 2019–2021 was projected at +0.71875% but rounded to +0.72% for reporting.

Abbreviations. DSP, dimension-specific price; ADP, all-dimension price.

Analysis

Equations (1)(2) $$\beta \mathrm{ }=\mathrm{ }\left(S{D}^2/\overline{X}\right)$$(2) and (2)(2) $$\beta \mathrm{ }=\mathrm{ }\left(S{D}^2/\overline{X}\right)$$(2) were applied to average scenario prices and its SD in each contract. Next, we performed MCS to generate price iterations (1,000, 2,000, etc.). The average of these iterations is considered the price point when all dimensions are integrated. This price is also denoted as the ADP, which represents the price of a drug when all dimensions and implied scenarios are integrated to generate a single monetary value (price) for a drug. We also assessed the influence of the six dimensions on the ADP by examining the payback ranges assumed in each dimension with changes in the ADP estimate in each of the two contracts.

Results

As shown in Table 2, all prices generated from the six dimensions resulted in an average of WAC for a 30-day prescription of osimertinib of $10,819 (SD=$8,486) for the 1-year contract and $10,730 (SD=$8,500) for the 2-year contract. The ADP was estimated from the MCS for the 1-year contract at $10,959 corresponding to a decrease of −25.02% relative to the 2018 WAC price of osimertinib. The ADP estimate for the 2-year contract was $10,788 corresponding to a decrease of −26.19% relative to the 2018 WAC price of osimertinib. Price differential percentages can assumed as payback percentages to be the statistically estimated for outcome-based contracting for a 30-day prescription of osimertinib using our six-dimensional platform.

In Figures 2 and 3, the six dimensions are scaled on the X-axis, starting from the bottom, while the Y-axis presents the scale for price adjustments relative to the 2018 WAC price for osimertinib. The middle line in the figures indicates no adjustment (zero on the Y-axis); with increases from zero to the right and decreases from zero to the left.

Figure 2. Price variations for a 1-year contract (2019–2020).

Figure 3. Price variations for 2-year contract (2019–2021).

Figure 2 shows results for the 1-year contract. For the first dimension (cost-effectiveness analysis and cost-utility analysis-based pricing), the percentage of price adjustment ranges from −100% to +185.07%, and the DSP is +12.14%. For the second dimension (willingness-to-pay-based pricing), the percentage of price adjustment ranges from −100% to +0.44%, and the DSP is −68.89%. For the third dimension (reference-based pricing), the percentage of price adjustment ranges from −61.72% to −6.18%, and the DSP is −35.72%. For the fourth dimension (safety-based pricing), the percentage of price adjustment ranges from −0.06% to +0.18%, and the DSP is +0.08%. For the fifth dimension (risk of efficacy failure-based pricing), the percentage of price adjustment ranges from −37.26% to +0.44%, and the DSP is −13.44%. For the sixth dimension (adherence-based pricing), the percentage of price adjustment ranges from −17.92% to +0.44%, and the DSP is −5.69%.

Figure 3 shows results for the 2-year contract. For the first dimension (cost-effectiveness analysis and cost-utility analysis-based pricing), the percentage of price adjustment ranges from −100% to +185.87%, and the DSP is 14.10%. For the second dimension (willingness-to-pay-based pricing), the percentage of price adjustment ranges from −100% to 0.72%, and the DSP is −67.82%. For the third dimension (reference-based pricing), the percentage of price adjustment ranges from −61.44% to −5.91%, and the DSP is −35.40%. For the fourth dimension (safety-based pricing), the percentage of price adjustment ranges from −1.11% to −0.26%, and the DSP is −0.68%. For the fifth dimension (risk of efficacy failure-based pricing), the percentage of price adjustment for osimertinib ranges from −35.08% to 0.72%, and the DSP is −10.93%. For the sixth dimension (adherence-based pricing), the percentage of price adjustment ranges from −40.31% to 0.72%, and the DSP is −13.92%.

Figures 2 and 3 show that both in the in 1-year contract (2019–2020) and 2-year contract (2019–2021), dimension 1 (cost-effectiveness analysis and cost-utility analysis-based pricing) was the most influential dimension in the ADP estimate, followed by dimension 2 (willingness-to-pay-based pricing), dimension 3 (reference-based pricing). Dimension 5 (risk of efficacy failure-based pricing) was the fourth most influential dimension for 1-year contract but the fifth influential dimension in 2-year contract. Dimension 6 (adherence-based pricing) was the fifth influential dimension in 1-year contract but the fourth influential dimension in 2-year contract. Dimension 4 (safety-based pricing) was the least influential dimension in both contracts.

Discussion

Outcome-based contracting is currently used by drug manufacturers and payers to reform traditional purchasing arrangements for pharmaceuticals. Contracting based on drug treatment outcomes is one attempt to reduce the cost of drugs, extend affordability, and improve the quality of care. A outcome-based contract assumes conditions in which drug payments are based on efficacy and safety performance¹².

Since the launch of outcome-based contracting, drug manufacturers and payers have faced challenges regarding which outcomes should be considered in and how the selected outcomes should be monitored and reported. With outcome-based contracting, manufacturers aim to increase the likelihood of being paid and reduce the likelihood of refunding payers. In contrast, drug payers aim to reduce the likelihood of paying the full price and increase the likelihood of receiving reimbursements or paybacks. In the end, the objective is to have a transparent, validated, quantitatively derived, and reliable approach to outcome-based contracting.

In the U.S, 39 outcome-based contracts were signed between drug manufacturers and drug payers between 2009 to the first quarter of 2018. Interestingly, these contracts were based on either single or multiple outcomes¹³ but without the systematicity and diversity in methods that we proposed here. In these contracts, outcomes seemed to have been based on a consensus between manufacturers and payers and to have been designed internally between both parties. Our proposed 6 δ’s platform offers a pragmatic multi-method approach for designing, implementing, and monitoring outcome-based contracts. To evaluate the value of such contracts, public access to the methods used to design these contracts and the specifications of these contracts are critical for evaluating the pragmatic value of these contracts¹⁴.

Out platform is characterized by comprehensiveness and independency of contracting. As to comprehensiveness, our platform incorporates scenarios related to cost-effectiveness and cost-utility; prices in foreign countries; safety; risk of efficacy failure; and adherence. Independency refers to the platform serving as a neutral quantitative environment that enables balanced, fair, and transparent contracting.

As shown in Table 2, all prices generated from the six-dimensional analyses yielded an average price for a 30 day prescription of osimertinib of $10,819 (SD=$8,486) for the 1-year contract and $10,730 (SD=$8,500) for the 2-year contract. The ADP was estimated for the 1-year contract at $10,959 or −25.02% the 2018 WAC); while the estimate for the 2-year contract was $10,788 or −26.19% the 2018 WAC price.

The ADP estimates reported here reflect the integration of all DSPs evaluated individually in our platform (Figure 1). Table 3 summarizes key characteristics of our platform. It includes all the currently relevant variables and dimensions for outcome-based contracting: efficacy, utility, and safety of a treatment of interest independently and relative to its comparators; willingness-to-pay for the treatment under restricted and unrestricted budgets; whether drug is in a monopolistic or competitive market; comparative pricing, whether relative to other countries and other markets or whether relative to competitors; the risk of treatment failure, and patient adherence as mediated by payers adopting manufacturer-sponsored adherence-enhancing program.

Table 3. Summary of variables included in the six dimensions.

	Dimension	Efficacy		Safety		Willingness-to-pay		Market environment		Other pricing factors		Value of insurance
		Independent	Relative to comparator(s)	Independent	Relative to comparator(s)	Restricted	Unrestricted	Monopolistic	Competitive	Relative to foreign price	Relative to alternative price	Risk of efficacy failure	Adherence support
Long-term assessment	DSPCEA/CUA		✓		✓						✓
	DSPWTP	✓	✓	✓	✓	✓	✓	✓	✓
Short term assessment	DSPReference									✓
	DSPSafety			✓	✓							✓
	DSPRisk of efficacy failure		✓									✓
	DSPAdherence	✓											✓
Long- and short-term assessment	ADP	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓

In our proof-of-concept applications, all six methods are weigthed equally in estimating the ADP. Our aim was to develop a multidimensional platform for outcome-based pricing without favoring one method over others. Contracting parties (payers and manufacturers) may assign a weight to each dimensional analysis.

Our platform can be adapted to other data sources and enables the integration of other variables. Technically, the methods can accommodate any source of data, including clinical trial data and real-world data, and can incorporate additional variables. While, ideally, real-world data should be incorporated, especially in the adherence dimension, the platform support multi-dimensional outcome-based pricing at the time of a drugs’ market entry.

The American Cancer Society (ACS) predicts that the incidence of small and NSCLC will be 228,150 in the U.S. in 2019 including 84% (est. 191,646) with NSCLC¹⁵. With the EGFR mutation at T790M detected in at least 50% of patients (∼95,823 patients), who would be candidates for osimertinib^16,17. Savings can be of $4.2 billion for the first year (1-year contract), with an additional savings of $4.4 billion if the contractors extend the outcome-based contract for another year (2-year contract). It is important to note that these savings can been made before the collection of real-world data. Also, these savings can be higher if considering prevalence data about the candidates who are eligible to switch to osimertinib.

Conclusion

Continuing our effort to develop six dimensions for outcome-based contracting treatments, we have integrated all of the price scenarios generated from the six-dimensional studies in our framework (Figure 1). This generated a single price (ADP) for osimertinib that considers all price dispersions among the dimensions and methods. Future innovations will focus on incorporating potentially new dimensions, scenarios, and variables.

Transparency

Declaration of funding

The work reported herein was performed without sponsorship or grant funding.

Declaration of financial/other relationships

The authors have no financial relationships to declare.

JME peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship of this manuscript.

Acknowledgements

No assistance was received in the preparation of this article.