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Enregistrement W7070479385

Patiëntenvoorkeuren om de waarde van gentherapie te bepalen

2020· article· en· W7070479385 sur OpenAlexaboutno aff

Notice bibliographique

RevueLirias (KU Leuven) · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMachine Learning in Bioinformatics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Orphan drugHealth technologyValue (mathematics)Quality of life (healthcare)Preference
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Drug development is increasingly focusing on precision medicine, Advanced Therapy Medicinal Products (ATMPs) including gene therapies, and orphan drugs. With the rise of gene therapies, that may have the potential to create permanent effects in patients, decision-making (on the level of marketing authorization, health technology assessment (HTA) and payer decision making, and shared-decision making between patients and physicians) will increasingly have to deal with uncertainty regarding long-term outcomes. In addition, the quality adjusted life years (QALY) is often used in HTA as a measure, but may not cover all aspects of gene therapies relevant to patients; possibly resulting in an under-estimation of the value of such therapies. In this context performing patient preference studies may inform decision-making by providing additional insights on the acceptability of uncertainties to patients and on the general value of these therapies to patients. However, it remained unknown what method to use for, and how to design and conduct, patient preference studies in the context of rare diseases, gene therapies and HTA. This PhD project aimed to investigate how patient preferences can be elicited and used in HTA and payer decision making regarding gene therapies for rare diseases. To reach this aim, the PhD project was divided into four parts. Part I provides an overview of challenges that can occur in the process of obtaining market access for gene therapies. Moreover, the literature review conducted for this part explored when challenges can arise, differences in the occurrence of challenges between jurisdictions (Europe, the US and Canada), trends among main challenges, and solutions. To date, experience with these products proved to be limited. A myriad of challenges was identified that inhibit market access of gene therapies in the EU, US and Canada. The 10 most-frequently mentioned issues were 'Difficulties in meeting regulatory quality requirements', 'Small clinical trials sample sizes', 'Single-arm vs. randomized controlled trial', 'Short-term clinical trials', 'Inappropriate selection of endpoints', 'Limited efficacy and effectiveness data', 'Uncertainty in long-term benefits', 'High short-term costs', 'Issues in valuation of benefits', and 'Classification and definition issues'. Many challenges were identified across multiple jurisdictions and decision-making contexts, but certain challenges seemed to be specific to legislations or decision-making contexts. Regarding reimbursement issues for example, issues caused by a multi-payer system were specific for the US and cross-border access was identified as a specific challenge for Canada. Challenges also seemed to be highly interlinked and some of these challenges were not unique to gene therapies, but also apply to other products. The importance of these challenges will vary according to the specific therapy being developed, and the country where market access is sought. For many challenges potential solutions were suggested in the literature such as the use of patient preferences in value assessments to address valuation issues, novel payment models to address the societal high short-term costs, and support platforms and programs for developers to help them in overcoming the different market access hurdles. Part II explores how patient preference studies should be designed and conducted to allow for inclusion of patient preferences in decision-making along the medical product life cycle (MPLC), and how patient preferences can be used in such decision-making. An initial overview of factors to consider when designing and conducting patient preference studies to inform decision making was obtained in in a literature review. This overview was then further refined in interviews, and clarified in focus groups with healthcare stakeholders. Healthcare stakeholders included patients, caregivers, and patient representatives, physicians, academics, industry representatives, regulators, and HTA/payer representatives. Factors and situations related to the organization, design, and conduct of studies, and to communication and use of results. The value of patient preferences for decision-making seemed to depend on the level of collaboration across stakeholders (including involvement of patients); the match between the research question, MPLC phase, sample, and preference method used; and the sensitivity of the decision regarding a medical product to patient preferences. Current use of patient preferences was found to be limited, but possible applications were identified in discovery, clinical development, marketing authorization, HTA and post-marketing phases. The most promising applications included: 1) benefit-risk assessment, 2) assessment of major contribution to patient's care, 3) cost-effectiveness analysis, and 4) multi criteria decision analysis. Part III describes a preference case study, namely the 'Patient preferences to Assess Value IN Gene therapies' (PAVING) study. This case study was designed to unveil how a patient preference study can be conducted in the context of gene therapies. Belgian hemophilia A and B patients were interviewed and subsequently participated in a survey to explore their preferences. The survey was designed following an eight-step approach where throughout the selection of the method and attributes as well as the design of an educational tool special attention was given to the innovative nature of gene therapies and the differences in features between these and alternative therapies, to the rarity of the disease, and to the needs of HTA and payers as this study aimed to inform their decision making. Part IV reports on a last study that investigated HTA representatives' perspectives on how to concretely incorporate patient preferences in HTA. For this purpose, three focus groups were conducted with HTA representatives from Germany, Belgium and Canada on how patient preferences can be used in HTA, with emphasis on the HTA stage, weight, impact and quality. Moreover, a gene therapy case was proposed to obtain opinions on a concrete example. An interest in the use of patient preferences was observed for scientific advice and value assessments, but not through incorporation in QALYs and MCDA. HTA representatives found it difficult to determine the weight patient preferences may receive in decision making, but thought it could have an impact on payer decision-making if the study is of acceptable quality. It was concluded that in the near future it may be impossible to achieve structural integration of patient preferences with other evidence in HTA (e.g. in cost-effectiveness analysis). Nevertheless, HTA bodies seemed willing to incorporate patient preferences in other HTA sections as supportive evidence and applicants should ensure HTA and payer needs are met when conducting patient preference studies to ensure acceptance of these studies. Results of the four parts were brought together in the discussion of this PhD to reveal 1) general considerations for patient preference studies aiming to inform decision making in the medical product lifecycle, 2) special considerations for patient preference studies aiming to inform health technology assessments of gene therapies for rare diseases, and 3) applications of patient preferences in health technology assessment and payer decision making. To guarantee that decision makers and researchers can keep up with the rapidly changing gene therapy access and patient preference research areas, the following, five key recommendations and areas of future research were proposed: 1. Ensure developers consider regulatory and payer needs in gene therapy development plans 2. Increase methodological guidance for patient preference studies 3. Enhance stakeholder involvement in patient preference studies 4. Invest in rare monogenic disease preference research and orphan drug access 5. Provide patient education on gene therapies

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,748
Score d'incertitude au seuil0,859

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,242
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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