Search PubMedSearch

PubMed · 42702686

Pricing Combination Therapies: A Systematic Review of Value Attribution, Cost-Sharing Mechanisms and Policy Frameworks.

Abstract

BACKGROUND: Combination therapies are increasingly central to modern pharmacotherapy, particularly in oncology and other high-burden diseases. However, pharmaceutical pricing and reimbursement systems remain largely designed for single-product-single-indication interventions. When multiple patented medicines are used together, especially when owned by different manufacturers, conventional pricing frameworks may struggle to align prices with the value of the combination while preserving incentives for innovation and timely patient access. OBJECTIVE: To identify, describe, and critically assess the methods, models, and policy frameworks proposed in the literature to establish prices for combination therapies, with particular attention to value attribution mechanisms, cost-sharing arrangements between manufacturers, and budget impact considerations. METHODS: A systematic literature review was conducted in accordance with PRISMA guidelines and a pre-registered Open Science Framework protocol. Searches were performed in MEDLINE, Scopus, Web of Science, EconLit, CRD databases, and grey literature sources for publications up to July 2025. Eligible studies analysed pricing approaches, economic models, reimbursement mechanisms, or policy frameworks relevant to combination therapies, including more recent multi-indication pricing literature. Given the heterogeneity of the literature, findings were synthesized using a structured narrative and thematic approach. RESULTS: Sixty-nine studies met the inclusion criteria. The literature was dominated by conceptual and policy analyses, with relatively few empirical or implementation-oriented studies. Value attribution emerged as the central methodological challenge in pricing combination therapies. Several complementary approaches were proposed to operationalise value attribution, including adaptations of indication- or pathway-based pricing, manufacturer cost-sharing arrangements, managed entry agreements, and outcome-based reimbursement mechanisms. Empirical evidence suggests that health systems continue to rely primarily on pragmatic and often partial solutions rather than fully specified pricing frameworks. A complementary review of the multi-indication pricing literature indicates that, although the two fields address different pricing problems, they share important methodological and institutional lessons that can inform the development of pricing frameworks for combination therapies. CONCLUSIONS: The literature provides a growing repertoire of conceptual approaches for pricing combination therapies but limited empirical evidence on implementation. Pricing frameworks should place value attribution at their core while combining complementary policy mechanisms adapted to national pricing and reimbursement systems. Lessons from multi-indication pricing provide a valuable foundation but require additional governance mechanisms to address value attribution, multi-manufacturer negotiation, and implementation challenges specific to combination therapies.

Explore related subjects

Keep this discovery

BibTeXRIS

Marta Trapero-Bertran, Carlos Martin-Saborido, Jorge Mestre-Ferrandiz, Nuria Amarilla, Lluís Segú. 2026-09-06. Pricing Combination Therapies: A Systematic Review of Value Attribution, Cost-Sharing Mechanisms and Policy Frameworks.. https://doi.org/10.1007/s40258-026-01065-z

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related citations

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence

What's the meta now? More updates on the problems with systematic reviews.

BACKGROUND: Systematic reviews are intended to provide trustworthy evidence synthesis, yet previous iterations of this living review have identified numerous recurring problems in their conduct and reporting. This article presents the third version and second update of the living systematic review examining issues raised across the academic literature. METHODS: Using consistent eligibility criteria and methods from earlier versions, literature searches were updated to May 2025. Eligible meta-research and editorial articles describing problems with systematic reviews were analyzed to identify emerging themes. Additionally, four basic indicators of methodological quality of the included meta-research were presented across review versions. RESULTS: The update included 209 additional articles. Critically low methodological quality and absence of protocols remained among the most frequently reported issues in systematic reviews across disciplines and journals but notably in evidence underpinning clinical practice guidelines. Spin in abstracts and conflicts of interest continued to be common. Apparent improvements in reporting quality were inconsistent, with modest gains in some full-text reporting but persistent deficiencies in abstracts. Authorship diversity of systematic reviews improved in gender representation but remained geographically concentrated in high-income countries, and primary research included in reviews similarly lacked global representativeness. The issue of misalignment between systematic review evidence bases and global burden of disease bring the total number of problems with systematic reviews to 69. Emerging use of automation and artificial intelligence was variably reported. Descriptive comparison of meta-research articles over the three versions of this living review suggests a greater proportion meeting basic quality indicators in more recent updates. CONCLUSION: Across successive updates, problems with systematic reviews remain widespread and consistent rather than isolated. Incremental reporting improvements coexist with persistent concerns about transparency, bias, and representativeness. Future efforts should prioritize evaluating interventions and aligning research incentives to support genuinely trustworthy evidence synthesis.

Humans