Evidence map›Paper›PMID 41403433›Full record

ArticleFrontiers in pharmacology2025

A hierarchical and configurational analysis of Health Technology Assessment outcomes for cell and gene therapies.

Ziyad S Almalki, Renad M Alshammari, Nadia A Dahduli, Mouaddh Abdulmalik Nagi, Syeda Juweria, Moayad M Alhamdani, Yazan M Alzahrani, Saja H Almazrou, Nehad Jaser Ahmed, Abdullah K Alahmari and 1 more

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ziyad S AlmalkiDepartment of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj/Riyadh, Saudi Arabia.
Renad M AlshammariDepartment of Clinical Pharmacy, College of Pharmacy, Almaarefa University, Ad Diriyah, Saudi Arabia.
Nadia A DahduliDepartment of Clinical Pharmacy, College of Pharmacy, Almaarefa University, Ad Diriyah, Saudi Arabia.
Mouaddh Abdulmalik NagiDepartment of Pharmacy, Faculty of Medical Sciences, Aljanad University for Science and Technology, Taiz, Yemen.
Syeda JuweriaDepartment of Clinical Pharmacy, College of Pharmacy, Almaarefa University, Ad Diriyah, Saudi Arabia.
Moayad M AlhamdaniDepartment of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj/Riyadh, Saudi Arabia.
Yazan M AlzahraniDepartment of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj/Riyadh, Saudi Arabia.
Saja H AlmazrouDepartment of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
Nehad Jaser AhmedDepartment of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj/Riyadh, Saudi Arabia.
Abdullah K AlahmariDepartment of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj/Riyadh, Saudi Arabia.
Areej A AlshlowiDepartment of Clinical Pharmacy, College of Pharmacy, Almaarefa University, Ad Diriyah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cell and gene therapies (CGTs) challenge traditional Health Technology Assessment (HTA), creating a fragmented global access landscape. This study identifies the determinants of CGT reimbursement outcomes by quantifying the influence of key variables and identifying the configurations leading to a positive recommendation. Methods: A dual-methodology approach was employed. We constructed a comprehensive dataset of all HTA decisions for CGTs across seven major jurisdictions between January 2017 and July 2025. Hierarchical Linear Modeling (HLM) was used to identify independent predictors of HTA outcomes, and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) was used to identify sufficient pathways to success. Novel composite indicators were developed to measure system-level adaptability and the influence of patient advocacy groups (PAGs). Results: The HLM analysis, accounting for data clustering (Intraclass Correlation Coefficients (ICCs): 42% country-level, 24% agency-level variance), confirmed that strong clinical efficacy (Coef. = 0.40), high unmet need, and disease rarity were significant positive predictors. High therapy cost was a powerful negative predictor (Coef. = -0.29 per $1M USD). Crucially, high System Adaptability (Coef. = 0.35) and strong PAG Influence (Coef. = 0.28) emerged as major positive determinants. The fsQCA revealed three distinct pathways to a positive recommendation with high consistency: a "Transformative Value" path (consistency: 0.93), a "Strategic Mitigation" path (consistency: 0.90), and an "Economic Dominance" path (consistency: 0.94). The overall QCA solution explained a majority of positive outcomes (solution coverage: 0.68). Conclusion: HTA success for CGTs is not determined by isolated attributes but by the strategic alignment of therapy-level evidence, agency-level processes, and country-level context. The influence of organized patient advocacy and the structural flexibility of HTA systems are critical, previously under-quantified components of this alignment.

Indexed as

cell and gene therapiesdecision-makingHealth Technology Assessmentreimbursementvalue

Identifiers

PMID41403433
PMCPMC12702701

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.