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Artículo en Inglés | MEDLINE | ID: mdl-33143182


BACKGROUND: Omics technologies, enabling the measurements of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics), are valuable tools for personalized decision-making. We aimed to identify the existing value assessment frameworks used by health technology assessment (HTA) doers for the evaluation of omics technologies through a systematic review. METHODS: PubMed, Scopus, Embase and Web of Science databases were searched to retrieve potential eligible articles published until 31 May 2020 in English. Additionally, through a desk research in HTA agencies' repositories, we retrieved the published reports on the practical use of these frameworks. RESULTS: Twenty-three articles were included in the systematic review. Twenty-two frameworks, which addressed genetic and/or genomic technologies, were described. Most of them derived from the ACCE framework and evaluated the domains of analytical validity, clinical validity and clinical utility. We retrieved forty-five reports, which mainly addressed the commercial transcriptomic prognostics and next generation sequencing, and evaluated clinical effectiveness, economic aspects, and description and technical characteristics. CONCLUSIONS: A value assessment framework for the HTA evaluation of omics technologies is not standardized and accepted, yet. Our work reports that the most evaluated domains are analytical validity, clinical validity and clinical utility and economic aspects.

Artículo en Inglés | MEDLINE | ID: mdl-32892765


INTRODUCTION: Precision medicines rely on companion diagnostics to identify patient subgroups eligible for receiving the pharmaceutical product. Until recently, the Belgian public health payer, RIZIV-INAMI, assessed precision medicines and companion diagnostics separately for reimbursement decisions. As both components are considered co-dependent technologies, their assessment should be conducted jointly from a health technology assessment (HTA) perspective. As of July 2019, a novel procedure was implemented accommodating for this joint assessment practice. The aim of this research was to formulate recommendations to improve the assessment in the novel procedure. METHODS: This study evaluated the precision medicine assessment reports of RIZIV-INAMI of the last 5 years under the former assessment procedure. The HTA framework for co-dependent technologies developed by Merlin et al. for the Australian healthcare system was used as a reference standard in this evaluation. Criteria were scored as either present or not present. RESULTS: Thirteen assessment reports were evaluated. Varying scores between reports were obtained for the domain establishing the co-dependent relationship between diagnostic and pharmaceutical. Domains evaluating the clinical utility of the biomarker and the cost-effectiveness performed poorly, whereas the budget impact and the transfer of trial data to the local setting performed well. RECOMMENDATIONS: Based on these results we recommend three amendments for the novel procedure. (i) The implementation of the linked evidence approach when direct evidence of clinical utility is not present, (ii) incorporation of a bias assessment tool, and (iii) further specify guidelines for submission and assessment to decrease the variability of reported evidence between assessment reports.

Value Health ; 23(5): 606-615, 2020 05.
Artículo en Inglés | MEDLINE | ID: mdl-32389226


OBJECTIVES: Ensuring access to precision medicine has been an issue because in some European countries, desynchronized reimbursement decision-making occurs between the medicine and the companion diagnostic (CDx). This has resulted in cases in which precision medicine is reimbursed but not the CDx. In overcoming this issue, an alignment of the decision-making process for reimbursement between the 2 entities should be considered. As pharmaceutical reimbursement procedures are meticulously covered in the literature, we set out to systematically map in vitro diagnostic (IVD) reimbursement procedures and identify policies for aligning these procedures with the pharmaceutical reimbursement procedures. METHODS: We selected 8 European countries for this analysis. For each country, we characterized the national benefit basket entailing the IVD medical acts in outpatient care, evaluated the procedure for inclusion, and identified alternative reimbursement practices for CDx. Targeted searches, using publicly accessible sources, were conducted to identify relevant reimbursement policies and laws. RESULTS: We systematically describe the reimbursement process in 8 European countries. Alternative procedures for CDx reimbursement were identified in Belgium and Germany. Alternative policies attributed to the practice of precision medicine were identified in England and Italy. In France, some CDx are included in the "coverage with evidence" development program. Specifically, the health technology assessment agencies of France and England commented on the assessment of companion diagnostics and their clinical utility. CONCLUSION: CDx reimbursement procedures have recently been implemented in some countries. This was seemingly done primarily to ensure access to the precision medicine and only secondary to the value they would provide.

Reembolso de Seguro de Salud/economía , Medicina de Precisión/economía , Medicina Estatal/economía , Evaluación de la Tecnología Biomédica/economía , Inglaterra , Europa (Continente) , Política de Salud , Humanos
Sci Rep ; 10(1): 964, 2020 01 22.
Artículo en Inglés | MEDLINE | ID: mdl-31969627


Nearest Neighbour (NN) propensity score (PS) matching methods are commonly used in pharmacoepidemiology to estimate treatment response using observational data. Unfortunately, there is limited evidence on the optimal approach for accurately estimating binary treatment response and, more so, to estimate its variance. Bootstrapping, although commonly used to accurately estimate variance, is rarely used together with PS matching. In this Monte Carlo simulation-based study, we examined the performance of bootstrapping used in conjunction with PS matching, as opposed to different NN matching techniques, on a simulated dataset exhibiting varying levels of real world complexity. Thus, an experimental design was set up that independently varied the proportion of patients treated, the proportion of outcomes censored and the amount of PS matches used. Simulation results were externally validated on a real observational dataset obtained from the Belgian Cancer Registry. We found all investigated PS methods to be stable and concordant, with k-NN matching to be optimally dealing with the censoring problem, typically present in chronic cancer-related datasets, whilst being the least computationally expensive. In contrast, bootstrapping used in conjunction with PS matching, being the most computationally expensive, only showed superior results in small patient populations with long-term largely unobserved treatment effects.

Antineoplásicos/uso terapéutico , Simulación por Computador , Neoplasias/tratamiento farmacológico , Humanos , Método de Montecarlo , Puntaje de Propensión , Sistema de Registros , Resultado del Tratamiento
Front Med (Lausanne) ; 6: 43, 2019.
Artículo en Inglés | MEDLINE | ID: mdl-30906740


Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

Front Pharmacol ; 10: 1665, 2019.
Artículo en Inglés | MEDLINE | ID: mdl-32116674


Objectives: Little research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide. Methods: Based on a retrospective observational registry covering 3090 patients with glioblastoma treated with temozolomide, we proposed the use of a two-step iterative exploratory learning process consisting of an initialization phase and a machine learning phase. For initialization, we defined a binary response variable as the target label using one-by-one nearest neighbor propensity score matching. Secondly, a classification tree algorithm was trained and validated for dividing individual patients into treatment response and non-response groups. Theorizing about treatment response was then done by evaluating the tree performance. Results: The classification tree model has an area under the curve (AUC) classification performance of 67% corresponding to a sensitivity of 0.69 and a specificity of 0.51. This result in predicting patient-level response was slightly better than the logistic regression model featuring an AUC of 64% (0.63 sensitivity and 0.54 specificity). The tree confirms confounding by age and discovers further age-related stratification with chemotherapy-treatment dependency, both not revealed in preceding clinical studies. The model lacked genetic information confounding treatment response. Conclusions: A classification tree was found to be suitable for understanding patient-level effectiveness for this glioblastoma-temozolomide case because of its high interpretability and capability to deal with covariate interdependencies, essential in a real-world environment. Possible improvements in the model's classification can be achieved by including genetic information and collecting primary data on treatment response. The model can be valuable in clinical practice for predicting personal treatment pathways.