Market Access & HEOR
Benefit-Risk Evidence for Payers, HTA Bodies, and Market Access Decisions
From pharmacovigilance data to value dossiers — extending benefit-risk analysis beyond the regulator.
The audience for benefit-risk evidence extends beyond regulators. Health technology assessment bodies, payer organizations, and market access teams require comparative effectiveness data, safety-contextualized value propositions, and evidence packages that address their specific decision frameworks. ArcaScience's platform extends the same analytical rigor used for regulatory submissions into HEOR deliverables: value dossiers, comparative effectiveness summaries, and payer evidence packages that draw on the full depth of AS Profiling Base 100b.
The Challenge
Why Regulatory Evidence Alone Does Not Satisfy Payers
Health technology assessment bodies — NICE in the UK, G-BA in Germany, HAS in France, CADTH in Canada, PBAC in Australia — require comparative effectiveness data beyond what regulators request. Payer decision frameworks emphasize value: clinical benefit relative to cost, not just benefit versus risk. Real-world evidence is increasingly expected alongside clinical trial data. Yet the current approach in most pharmaceutical organizations is fragmented: separate HEOR teams re-analyze data that was already processed for regulatory purposes, using different tools, different methodologies, and different data extracts. This creates inconsistency between the regulatory narrative and the payer narrative — a gap that HTA assessors notice and penalize.
Fragmented Evidence Generation
Regulatory and HEOR teams work from different data extracts using different tools. The benefit-risk narrative presented to FDA/EMA diverges from the value narrative presented to NICE/G-BA, creating inconsistencies that erode credibility with both audiences.
Comparator Evidence Gaps
HTA bodies require head-to-head or indirect treatment comparisons that clinical development programs rarely generate. Building comparative effectiveness evidence retrospectively is expensive, time-consuming, and methodologically constrained.
Diverse HTA Requirements
Each HTA body has distinct evidence requirements, submission formats, and assessment frameworks. NICE's technology appraisals, G-BA's AMNOG added-benefit assessments, and HAS's transparency committee reviews each require tailored evidence packages.
How ArcaScience Addresses This
One Data Foundation, Multiple Evidence Packages
Comparative Effectiveness & RWE Integration
The Data Intelligence Engine draws from AS Profiling Base 100b to build comprehensive comparator datasets. Indirect treatment comparisons (ITCs) and network meta-analyses (NMAs) are supported by integrated real-world evidence from clinical trials, observational studies, registries, and post-marketing data — providing the comparative context that HTA bodies require but clinical development programs rarely generate directly.
Safety-Contextualized Value Analysis
Decision Intelligence extends the benefit-risk analysis produced for regulatory submissions into payer-relevant frameworks. Comparative effectiveness across active comparators, subpopulation-specific benefit-risk profiles relevant for tiered access decisions, and safety-contextualized value narratives that demonstrate how the product's safety profile supports its value proposition — all grounded in the same data foundation as the regulatory submission.
Value Dossiers & HTA Submissions
Generate structured value dossiers, HTA submission packages, and payer evidence summaries tailored to the requirements of specific assessment bodies. Budget impact and cost-effectiveness framing where underlying data supports it. The payer narrative maintains consistency with the regulatory submission — both draw from the same analytical framework and data foundation.
Workflow
How It Works
Extend Regulatory BRA for Payers
Start from the benefit-risk analysis produced for regulatory submission. The same data foundation and analytical framework are extended to address payer-specific questions about comparative effectiveness and value.
Build Comparative Evidence
The Data Intelligence Engine constructs indirect treatment comparisons and network meta-analyses across active comparators, integrating clinical trial data with real-world evidence to fill evidence gaps that HTA bodies identify.
Tailor for HTA Requirements
Adapt evidence presentation for specific HTA body requirements: NICE technology appraisals, G-BA added-benefit assessments (AMNOG), HAS transparency committee reviews, CADTH common drug reviews, and PBAC submissions.
Generate Value Dossiers
Produce structured value dossiers, payer evidence packages, and HTA submission documents. Budget impact framing and cost-effectiveness narratives are grounded in the same data and analysis as the regulatory submission.
Case Study — Takeda
Rare Disease Value Dossier for Multi-Market HTA Submissions
Takeda engaged ArcaScience to produce a value dossier for a rare disease compound requiring simultaneous HTA submissions across multiple markets. The platform's integrated data foundation enabled a single analytical workflow to generate tailored evidence packages for NICE, G-BA, and HAS — maintaining internal consistency while adapting to each body's distinct assessment requirements. Comparative effectiveness evidence was built from real-world data where head-to-head trials were not available, supported by indirect treatment comparisons constructed from the AS Profiling Base 100b.
Read Full Case Study →Director of Market Access
Takeda — Rare Disease Program
Related Solutions
Benefit-Risk Analysis Across the Lifecycle
Early Development
Phase I-II signal detection, DDI screening, comparator profiling, and early benefit-risk framing.
Late Development & Submission
CTD Module 2.5, advisory committee preparation, integrated benefit-risk summary for regulatory submission.
Post-Marketing Surveillance
Automated PSUR/PBRER generation, continuous signal monitoring, RMP updates, label change support.