Although personalized medicine has been a subject of research and debate in recent years, it has been underused in medical practice, except in some cancers. We believe that the main reason for the gap between the potential of personalized medicine and its use in daily medical practice can be explained by the lack of an appropriate tool to facilitate the use of biomarker values in a doctor's decision-making process. We propose that the effect model could form the basis of such a tool.
Background Randomised, double-blind, clinical trial methodology minimises bias in the measurement of treatment efficacy. However, most phase III trials in non-orphan diseases do not include individuals from the population to whom efficacy findings will be applied in the real world. Thus, a translation process must be used to infer effectiveness for these populations. Current conventional translation processes are not formalised and do not have a clear theoretical or practical base. There is a growing need for accurate translation, both for public health considerations and for supporting the shift towards personalised medicine. Objective Our objective was to assess the results of translation of efficacy data to population efficacy from two simulated clinical trials for two drugs in three populations, using conventional methods. Methods We simulated three populations, two drugs with different efficacies and two trials with different sampling protocols. Results With few exceptions, current translation methods do not result in accurate population effectiveness predictions. The reason for this failure is the non-linearity of the translation method. One of the consequences of this inaccuracy is that pharmacoeconomic and postmarketing surveillance studies based on direct use of clinical trial efficacy metrics are flawed. Conclusion There is a clear need to develop and validate functional and relevant translation approaches for the translation of clinical trial efficacy to the real-world setting. Electronic supplementary material The online version of this article (10.1007/s40801-019-0159-z) contains supplementary material, which is available to authorized users.
Healthcare authorities make difficult decisions about how to spend limited budgets for interventions that guarantee the best cost-efficacy ratio. We propose a novel approach for treatment decision-making, OMES-in French: Objectif thérapeutique Modèle Effet Seuil (in English: Therapeutic ObjectiveThreshold-Effect Model; TOTEM). This approach takes into consideration results from clinical trials, adjusted for the patients' characteristics in treatment decision-making. We compared OMES with the French clinical practice guidelines (CPGs) for the management of dyslipidemia with statin in a computergenerated realistic virtual population, representing the adult French population, in terms of the number of all-cause deaths avoided (number of avoided events: NAEs) under treatment and the individual absolute benefit. The total budget was fixed at the annual amount reimbursed by the French social security for statins. With the CPGs, the NAEs was 292 for an annual cost of 122.54 ME compared with 443 with OMES. For a fixed NAEs, OMES reduced costs by 50% (60.53 ME yr 21). The results demonstrate that OMES is at least as good as, and even better than, the standard CPGs when applied to the same population. Hence the OMES approach is a practical, useful alternative which will help to overcome the limitations of treatment decision-making based uniquely on CPGs.
The practice of evidence-based medicine requires a tool to assess and discriminate available data based on objective grounds, thus facilitating access to reliable information. The level of evidence, conceptually and practically embedded in scientific activity, allows comparing the results from multiple studies testing an identical hypothesis along the lines of at least two dimensions. The first dimension deals with the design of the study, i.e. the extent to which bias is avoided or managed, the second with the quality of incorporated data. A third dimension specific to therapeutic evaluation focuses on the clinical relevance of the tested hypothesis. The concern of the final user of the information is thus put to the fore. Indeed, a general practitioner will be interested in the benefit for its patients whereas the concern of a biologist might significantly diverge from the former matter. The bulk of existing scales of level of evidence concentrate on methodology. Some may include the second dimension but none embrace the three of them. Seldom considered are matters regarding reproducibility and procedure. This is all the more unfortunate as reproducibility is a cornerstone of scientific progress. Moreover, scales used for overviews fail to take into account the methodology designed to produce the synthesis. Inconsistent existing scales prevent the emergence of a generally agreed standard. Therefore, there is a need to further specify the concept of level of evidence in therapy evaluation and design scales encompassing the three above-mentioned dimensions: methodology of experiment, quality of data, and clinical relevance of the primary criterion.
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