BEYOND PICO PREDICTION: A SYSTEMATIC FRAMEWORK FOR EVIDENCE GAP ANALYSIS AND STRATEGIC PRIORITIZATION FOR EUROPEAN UNION JOINT CLINICAL ASSESSMENT (EU JCA) READINESS
Author(s)
Parth Joshi, M.Pharm.1, Chuyi Zhang, MSc.2, Lise Amoura, PharmD, MSc.3, Valéry Risson, MBA, PhD4.
1Value & Access, Business Solutions International, Novartis Healthcare Private Limited, Hyderabad, India, 2Global Health Economics and Health Technology Assessment, IQVIA, London, United Kingdom, 3Global Health Economics and Health Technology Assessment, IQVIA, Paris, France, 4International Value & Access, Novartis Pharma AG, Basel, Switzerland.
1Value & Access, Business Solutions International, Novartis Healthcare Private Limited, Hyderabad, India, 2Global Health Economics and Health Technology Assessment, IQVIA, London, United Kingdom, 3Global Health Economics and Health Technology Assessment, IQVIA, Paris, France, 4International Value & Access, Novartis Pharma AG, Basel, Switzerland.
OBJECTIVES: Patient population, intervention, comparator, and outcomes (PICO) scoping is central to the EU JCA process. However, methodologies for evidence-gap analysis and strategic prioritization based on anticipated PICOs have received limited attention. This study aimed to develop a systematic framework extending beyond PICO prediction to enable evidence-gap analysis and strategic prioritization for EU JCA, translating complex PICO requirements into actionable evidence-generation strategies.
METHODS: A three-step framework was developed and applied to a case study in metastatic castration-resistant prostate cancer: (a) country-specific PICOs were simulated across selected representative EU markets via a structured internal PICO survey, consolidated per EU HTA guidance, and assigned scores (low/medium and high) on their likelihood of being requested based on treatment patterns and HTA precedents; (b) existing evidence was mapped per PICO, and where gaps were identified, the feasibility of evidence synthesis (indirect comparison) was assessed. Evidence availability was categorized as sufficient existing evidence, additional evidence synthesis feasible, or additional evidence synthesis not feasible; (c) a 2×3 matrix was developed that integrated likelihood of PICO being requested and feasibility of generating robust evidence, creating six strategic quadrants for prioritization.
RESULTS: Multiple consolidated PICOs were identified, with 50% classified as high likelihood of being requested. Approximately one-third were considered to have sufficient direct evidence, while the remaining required indirect comparisons or alternative approaches with varying evidence generation feasibility. Strategic prioritization via the 2×3 matrix mapped PICOs into distinct quadrants, each with tailored tactical recommendations—from immediate evidence generation to stakeholder engagement, treatment landscape monitoring, contingency planning, and resource deferral. The framework systematically identified high-risk scenarios where likelihood and feasibility misalign, enabling proactive evidence planning.
CONCLUSIONS: Early PICO simulation and structured evidence-gap assessment can support proactive EU JCA readiness. A systematic framework for prioritizing anticipated evidence requirements may help health technology developers align evidence-generation strategies, optimize resource allocation, and improve JCA preparedness.
METHODS: A three-step framework was developed and applied to a case study in metastatic castration-resistant prostate cancer: (a) country-specific PICOs were simulated across selected representative EU markets via a structured internal PICO survey, consolidated per EU HTA guidance, and assigned scores (low/medium and high) on their likelihood of being requested based on treatment patterns and HTA precedents; (b) existing evidence was mapped per PICO, and where gaps were identified, the feasibility of evidence synthesis (indirect comparison) was assessed. Evidence availability was categorized as sufficient existing evidence, additional evidence synthesis feasible, or additional evidence synthesis not feasible; (c) a 2×3 matrix was developed that integrated likelihood of PICO being requested and feasibility of generating robust evidence, creating six strategic quadrants for prioritization.
RESULTS: Multiple consolidated PICOs were identified, with 50% classified as high likelihood of being requested. Approximately one-third were considered to have sufficient direct evidence, while the remaining required indirect comparisons or alternative approaches with varying evidence generation feasibility. Strategic prioritization via the 2×3 matrix mapped PICOs into distinct quadrants, each with tailored tactical recommendations—from immediate evidence generation to stakeholder engagement, treatment landscape monitoring, contingency planning, and resource deferral. The framework systematically identified high-risk scenarios where likelihood and feasibility misalign, enabling proactive evidence planning.
CONCLUSIONS: Early PICO simulation and structured evidence-gap assessment can support proactive EU JCA readiness. A systematic framework for prioritizing anticipated evidence requirements may help health technology developers align evidence-generation strategies, optimize resource allocation, and improve JCA preparedness.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
HTA104
Topic
Health Technology Assessment
Topic Subcategory
Systems & Structure
Disease
Oncology