A METHODOLOGICAL FRAMEWORK FOR IDENTIFYING PROGNOSTIC FACTORS AND TREATMENT EFFECT MODIFIERS FOR EU JOINT CLINICAL ASSESSMENT AND GLOBAL HTA
Author(s)
Sheily Kamra, MBA1, Ketevan Rtveladze, M.Sc in Health Economics2, Jelena Jovanovic, DPhil2, Edel Falla, MSc2, Tanushree Chaudhary Pavithran, Sr., M.Sc1, Yogesh Punekar, PhD in Health Economics and Outcomes Research2.
1IQVIA, Gurugram, India, 2IQVIA, London, United Kingdom.
1IQVIA, Gurugram, India, 2IQVIA, London, United Kingdom.
OBJECTIVES: To identify a methodologically robust yet operationally feasible approach for prognostic factor (PF) and treatment effect modifier (TEM) identification, aligned with JCA requirements and adaptable to global HTA.
METHODS: Review of methodological guidance from HTACG (including Q&A, May 2026), NICE, IQWiG, HAS, and CDA-AMC was conducted. The first JCA report (tovorafenib) was assessed alongside established prognosis-research frameworks (CHARMS-PF, QUIPS, REMARK).
RESULTS: Methodological expectations for identifying PFs and TEMs vary across HTA bodies (JCA, NICE, IQWiG, HAS, CDA-AMC), ranging from systematic reviews to expert-led approaches. The HTACG Q&A (May 2026) specifies that a systematic literature review (SLR) is the first step in covariate selection for comparative effectiveness analyses, requiring both randomised and observational evidence. However, applying the clinical SLR’s PICOS framework, particularly intervention restrictions, to PF identification may not be methodologically fit for purpose. A fully standalone PF/TEM SLR without intervention restrictions maximise rigour but increases workload and cost. The first JCA (tovorafenib) indicates that identification of prognostic variables and effect modifiers was informed by a targeted literature review (TLR) focusing on subgroup analyses from company’s pivotal trial and comparator trials, supplemented by SLR-derived evidence, grey literature, and expert input; however, this preceded updated HTACG requirements. Our review findings support a pragmatic dual‑track approach. TEMs can be identified from subgroup analyses of randomised controlled trials and meta-analyses within the clinical SLR, with credibility assessed using ICEMAN. PFs can be identified via targeted or semi-systematic review using EMBASE, single-reviewer screening with 10% random quality check, and the PICOTS framework (P=Population; I=index prognostic factor; C=comparator prognostic factor; T=timing of covariate measurement; S=setting) without intervention restrictions. Appraisal tools (QUIPS, CHARMS-PF, REMARK) and structured expert validation enhance robustness.
CONCLUSIONS: A dual-track strategy, combining clinical SLRs with targeted prognostic factor reviews, ensures methodologically robust and consistent PF/TEM identification across JCA and national HTA submissions to inform HTA decision-making.
METHODS: Review of methodological guidance from HTACG (including Q&A, May 2026), NICE, IQWiG, HAS, and CDA-AMC was conducted. The first JCA report (tovorafenib) was assessed alongside established prognosis-research frameworks (CHARMS-PF, QUIPS, REMARK).
RESULTS: Methodological expectations for identifying PFs and TEMs vary across HTA bodies (JCA, NICE, IQWiG, HAS, CDA-AMC), ranging from systematic reviews to expert-led approaches. The HTACG Q&A (May 2026) specifies that a systematic literature review (SLR) is the first step in covariate selection for comparative effectiveness analyses, requiring both randomised and observational evidence. However, applying the clinical SLR’s PICOS framework, particularly intervention restrictions, to PF identification may not be methodologically fit for purpose. A fully standalone PF/TEM SLR without intervention restrictions maximise rigour but increases workload and cost. The first JCA (tovorafenib) indicates that identification of prognostic variables and effect modifiers was informed by a targeted literature review (TLR) focusing on subgroup analyses from company’s pivotal trial and comparator trials, supplemented by SLR-derived evidence, grey literature, and expert input; however, this preceded updated HTACG requirements. Our review findings support a pragmatic dual‑track approach. TEMs can be identified from subgroup analyses of randomised controlled trials and meta-analyses within the clinical SLR, with credibility assessed using ICEMAN. PFs can be identified via targeted or semi-systematic review using EMBASE, single-reviewer screening with 10% random quality check, and the PICOTS framework (P=Population; I=index prognostic factor; C=comparator prognostic factor; T=timing of covariate measurement; S=setting) without intervention restrictions. Appraisal tools (QUIPS, CHARMS-PF, REMARK) and structured expert validation enhance robustness.
CONCLUSIONS: A dual-track strategy, combining clinical SLRs with targeted prognostic factor reviews, ensures methodologically robust and consistent PF/TEM identification across JCA and national HTA submissions to inform HTA decision-making.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MSR120
Topic
Clinical Outcomes, Methodological & Statistical Research, Study Approaches
Topic Subcategory
Confounding, Selection Bias Correction, Causal Inference
Disease
No Additional Disease & Conditions/Specialized Treatment Areas