In biomedical research, population differences are of central interest. Variations in the frequency and severity of diseases and in treatment effects among human subpopulation groups are common in many medical conditions. Unfortunately, the practices in terms of subpopulation labeling do not exhibit the level of rigor one would expect in biomedical research, especially when studying multifactorial diseases such as cancer or atherosclerosis. The reporting of population differences in clinical research is characterized by large disparities in practices, and fraught with methodological issues and inconsistencies. The actual designations such as "Black" or "Asian" refer to broad and heterogeneous groups, with a great discrepancy among countries. Moreover, the use of obsolete concepts such as "Caucasian" is unfortunate and imprecise. The use of adequate labeling to reflect the scientific hypothesis needs to be promoted. Furthermore, the use of "race/ethnicity" as a unique cause of human heterogeneity may distract from investigating other factors related to a medical condition, particularly if this label is employed as a proxy for cultural habits, diet, or environmental exposure. In addition, the wide range of opinions among researchers does not facilitate the attempts made for resolving this heterogeneity in labeling. "Race," "ethnicity," "ancestry," "geographical origin," and other similar concepts are saturated with meanings. Even if the feasibility of a global consensus on labeling seems difficult, geneticists, sociologists, anthropologists, and ethicists should help develop policies and practices for the biomedical field.
Abstract This study aimed to explore the validity of the use of the net clinical benefit (NCB), i.e. the sum of major bleeding and thrombotic events, as a potential surrogate for all-cause mortality in clinical trials assessing antithrombotics. Published randomized controlled trials testing anticoagulants in the prevention or treatment of venous thromboembolism (VTE) and non-valvular atrial fibrillation (NVAF) were systematically reviewed. The validity of NCB as a surrogate endpoint was estimated by calculating the strength of correlation of determination (R 2 ) and its 95% confidence interval (CI) between the relative risks of NCB and all-cause mortality. Amongst the 125 trials retrieved, the highest R 2 trial values were estimated for NVAF (R 2 trial = 0.41, 95% CI [0.03; 0.48]), and acute VTE (R 2 trial = 0.30, 95% CI [0.04; 0.84]). Conversely, the NCB did not correlate with all-cause mortality in prevention studies with medical (R 2 trial = 0.12, 95% CI [0.00; 0.36]), surgical (R 2 trial = 0.05, 95% CI [0.00; 0.23]), and cancer patients (R 2 trial = 0.006, 95% CI [0.00; 1.00]). A weak correlation between NCB and all cause-mortality was found in NVAF and acute VTE, whereas no correlation was observed in clinical situations where the mortality rate was low. Consequently, NCB should not be considered a surrogate outcome for all cause-mortality in anticoagulation trials.
Introduction. La médecine fondée sur les preuves (EBM) est la pierre angulaire de la décision médicale partagée. Le public mérite des informations claires, transparentes et dignes de confiance sur l’efficacité des médicaments. Pourtant, aujourd’hui, de nombreux médicaments sont prescrits et utilisés sans preuve solide de leur efficacité. Les essais cliniques randomisés (ECR) et leurs méta-analyses sont les meilleures études pour évaluer l’efficacité des médicaments et leurs effets indésirables, mais leurs résultats ne sont pas facilement interprétables en pratique et sont même parfois discutables par rapport aux données retenues. Dans une approche de décision médicale partagée, les médecins généralistes ont besoin que l’évaluation des médicaments soit fondée sur des résultats importants et pertinents pour le patient. L’objectif du projet Rebuild the Evidence Base (REB) est de combler le fossé entre les données nécessaires à la pratique clinique et les données disponibles de la recherche clinique. Méthodes et analyses. Les médicaments seront évalués selon des critères cliniques importants pour les patients et dans une population donnée. En utilisant les outils Cochrane, pour chaque population et critère d’évaluation choisis, seront réalisées : 1. une méta-analyse, fondée sur des essais contrôlés randomisés (ECR) avec un faible risque global de biais ; 2. l’évaluation des résultats issus des ECR de confirmation ; 3. l’évaluation de l’hétérogénéité statistique entre essais (I2), et 4. l’évaluation du risque de biais de publication. En fonction des résultats de ces analyses, les preuves seront évaluées selon quatre niveaux : preuve solide, résultat probant mais à confirmer, signal à confirmer, ou absence de preuve. Conclusion. Le projet REB propose une méthode de lecture et d’interprétation des essais cliniques randomisés et de leur méta-analyse afin de produire des données de qualité permettant aux médecins généralistes de se centrer sur l’évaluation du bénéfice-risque dans l’intérêt des patients. Si ces données n’existent pas, cela permettra à la recherche clinique de mieux définir ses objectifs.
Abstract Background One key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach. Methods We compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c -statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo ( $$N = 40\,237$$ N=40237 , 836 events). Results Simulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances. Conclusions For the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.
Abstract We report here the state of development of photoemission diffraction from core levels as a tool for the investigation of surfaces. Experimental procedures for performing reproducible and accurate photoemission diffraction curves are formulated. To this end, the merit and the limitations in the choice of different parameters, such as photoelectron kinetic energy or detection angles, are discussed in view of various applications. The discussion is largely exemplified from our experiences, as well as from those in the literature.
The surface termination of the (111) face of a ${\mathrm{CoSi}}_{2}$ bulk crystal is determined through a comparison between experimental results from x-ray photoelectron diffraction and theoretical multiple-scattering calculations for different surface models. It is found that the bulk crystal is terminated by only one Si layer as opposed to epitaxially grown ${\mathrm{CoSi}}_{2}$ films on Si(111) which can exhibit surfaces with one or more Si layers, depending on the epitaxial growth conditions. Confirmation of the results is found through analysis of data from photoelectron diffraction at low photon energy (\ensuremath{\sim}120 eV).
