Barozh 12: formation processes of a late Middle Paleolithic open-air site in western Armenia

© 2020 Elsevier Ltd Barozh 12 is a Middle Paleolithic (MP) open-air site located near the Mt Arteni volcanic complex at the margins of the Ararat Depression, an intermontane basin that contains the Araxes River. Sedimentology, micromorphology, geochronology, biomarker evidence, together with an assessment of artifact taphonomy permits the modelling of site formation processes and paleoenvironment at a level of detail not previously achieved in this area. Obsidian MP artifacts were recovered in high densities at Barozh 12 from four stratigraphic units deposited during marine oxygen isotope stage 3 (MIS 3) (60.2 ± 5.7–31.3 ± 3 ka). The MIS 3 sequence commences with low energy alluvial deposits that have been altered by incipient soil formation, while artifact assemblages in these strata were only minimally reworked. After a depositional hiatus, further low energy alluvial sedimentation and weak soil formation occurred, followed by higher energy colluvial (re)deposition and then deflation. Artifacts in these last stratigraphic units were more significantly reworked than those below. Analysis of plant leaf wax (n-alkane) biomarkers shows fluctuating humidity throughout the sequence. Collectively the evidence suggests that hunter-gatherers equipped with MP lithic technology repeatedly occupied this site during variable aridity regimes, demonstrating their successful adaptation to the changing environments of MIS 3

Development and Validation of a New Hierarchical Composite End Point for Clinical Trials of Kidney Disease Progression

BACKGROUND: The established composite kidney end point in clinical trials combines clinical events with sustained large changes in GFR. However, the statistical method does not weigh the relative clinical importance of the end point components. A HCE accounts for the clinical importance of the end point components and enables combining dichotomous outcomes with continuous measures. METHODS: We developed and validated a new HCE for kidney disease progression, performing post hoc analyses of seven major Phase 3 placebo-controlled trials that assessed the effects of canagliflozin, dapagliflozin, finerenone, atrasentan, losartan, irbesartan, and aliskiren in patients with CKD. We calculated the win odds (WOs) for treatment effects on a kidney HCE, defined as a hierarchical composite of all-cause mortality; kidney failure; sustained 57%, 50%, and 40% GFR declines from baseline; and GFR slope. The WO describes the odds of a more favorable outcome for receiving the active compared with the control. We compared the WO with the hazard ratio (HR) of the primary kidney outcome of the original trials. RESULTS: In all trials, treatment effects calculated with the WO reflected a similar direction and magnitude of the treatment effect compared with the HR. Clinical trials incorporating the HCE would achieve increased statistical power compared with the established composite end point at equivalent sample sizes. CONCLUSIONS: In seven major kidney clinical trials, the WO and HR provided similar direction of treatment effect estimates with smaller HRs associated with larger WOs. The prioritization of clinical outcomes and inclusion of broader composite end points makes the HCE an attractive alternative to the established kidney end point

Dapagliflozin across the range of ejection fraction in patients with heart failure:a patient-level, pooled meta-analysis of DAPA-HF and DELIVER

Whether the sodium-glucose cotransporter 2 inhibitor dapagliflozin reduces the risk of a range of morbidity and mortality outcomes in patients with heart failure regardless of ejection fraction is unknown. A patient-level pooled meta-analysis of two trials testing dapagliflozin in participants with heart failure and different ranges of left ventricular ejection fraction (40%) was pre-specified to examine the effect of treatment on endpoints that neither trial, individually, was powered for and to test the consistency of the effect of dapagliflozin across the range of ejection fractions. The pre-specified endpoints were: death from cardiovascular causes; death from any cause; total hospital admissions for heart failure; and the composite of death from cardiovascular causes, myocardial infarction or stroke (major adverse cardiovascular events (MACEs)). A total of 11,007 participants with a mean ejection fraction of 44% (s.d. 14%) were included. Dapagliflozin reduced the risk of death from cardiovascular causes (hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.76-0.97; P = 0.01), death from any cause (HR 0.90, 95% CI 0.82-0.99; P = 0.03), total hospital admissions for heart failure (rate ratio 0.71, 95% CI 0.65-0.78; P &lt; 0.001) and MACEs (HR 0.90, 95% CI 0.81-1.00; P = 0.045). There was no evidence that the effect of dapagliflozin differed by ejection fraction. In a patient-level pooled meta-analysis covering the full range of ejection fractions in patients with heart failure, dapagliflozin reduced the risk of death from cardiovascular causes and hospital admissions for heart failure (PROSPERO: CRD42022346524).A pre-specified meta-analysis of pooled, individual patient-level data from the DELIVER and DAPA-HF trials, testing dapagliflozin in patients with heart failure, demonstrates reductions in risk of cardiovascular-associated deaths and hospital admissions for heart failure, regardless of ejection fraction.</p

Dapagliflozin and Kidney Outcomes in Hospitalized Patients with COVID-19 Infection:An Analysis of the DARE-19 Randomized Controlled Trial

Background and objectives: Patients who were hospitalized with coronavirus disease 2019 (COVID-19) infection are at high risk of AKI and KRT, especially in the presence of CKD. The Dapagliflozin in Respiratory Failure in Patients with COVID-19 (DARE-19) trial showed that in patients hospitalized with COVID-19, treatment with dapagliflozin versus placebo resulted in numerically fewer participants who experienced organ failure or death, although these differences were not statistically significant. We performed a secondary analysis of the DARE-19 trial to determine the efficacy and safety of dapagliflozin on kidney outcomes in the overall population and in prespecified subgroups of participants defined by baseline eGFR. Design, setting, participants, & measurements: The DARE-19 trial randomized 1250 patients who were hospitalized (231 [18%] had eGFR <60 ml/min per 1.73 m2) with COVID-19 and cardiometabolic risk factors to dapagliflozin or placebo. Dual primary outcomes (time to new or worsened organ dysfunction or death, and a hierarchical composite end point of recovery [change in clinical status by day 30]), and the key secondary kidney outcome (composite of AKI, KRT, or death), and safety were assessed in participants with baseline eGFR <60 and ≥60 ml/min per 1.73 m2. Results: The effect of dapagliflozin versus placebo on the primary prevention outcome (hazard ratio, 0.80; 95% confidence interval, 0.58 to 1.10), primary recovery outcome (win ratio, 1.09; 95% confidence interval, 0.97 to 1.22), and the composite kidney outcome (hazard ratio, 0.74; 95% confidence interval, 0.50 to 1.07) were consistent across eGFR subgroups (P for interaction: 0.98, 0.67, and 0.44, respectively). The effects of dapagliflozin on AKI were also similar in participants with eGFR <60 ml/min per 1.73 m2 (hazard ratio, 0.71; 95% confidence interval, 0.29 to 1.77) and ≥60 ml/min per 1.73 m2 (hazard ratio, 0.69; 95% confidence interval, 0.37 to 1.29). Dapagliflozin was well tolerated in participants with eGFR <60 and ≥60 ml/min per 1.73 m2. Conclusions: The effects of dapagliflozin on primary and secondary outcomes in hospitalized participants with COVID-19 were consistent in those with eGFR below/above 60 ml/min per 1.73 m2. Dapagliflozin was well tolerated and did not increase the risk of AKI in participants with eGFR below or above 60 ml/min per 1.73 m2

Dapagliflozin and recurrent heart failure hospitalizations in heart failure with reduced ejection fraction: an analysis of DAPA-HF

Background: Patients with heart failure and reduced ejection fraction (HFrEF) will experience multiple hospitalizations for heart failure during the course of their disease. We assessed the efficacy of dapagliflozin on reducing the rate of total (i.e. first and repeat) hospitalizations for heart failure in the Dapagliflozin and Prevention of Adverse-outcomes in Heart Failure trial (DAPA-HF). Methods: The total number of HF hospitalizations and cardiovascular deaths was examined using the proportional rates approach of Lei-Wei-Ying-Yang (LWYY) and a joint frailty model for each of recurrent HF hospitalizations and time to cardiovascular death. Variables associated with the risk of recurrent hospitalizations were explored in a multivariable LWYY model. Results: Of 2371 participants randomized to placebo, 318 experienced 469 hospitalizations for heart failure among; of 2373 assigned to dapagliflozin, 230 patients experienced 340 admissions. In a multivariable model factors associated with a higher risk of recurrent HF hospitalizations included higher heart rate, higher NT-proBNP and NYHA class. In the LWYY model the rate ratio for the effect of dapagliflozin on recurrent HF hospitalizations or CV death was 0.75 (95%CI 0.65-0.88), p=0.0002. In the joint frailty model, rate ratio for total HF hospitalizations was 0.71 (95% CI 0.61-0.82), p&lt;0.0001 while for cardiovascular death the hazard ratio was 0.81(95%CI 0.67-0.98), p=00282. Conclusions: Dapagliflozin reduced the risk of total (first and repeat) HF hospitalizations and cardiovascular death. Time-to-first event analysis underestimated the benefit of dapagliflozin in HFrEF. Clinical Trial Registration: URL: https://www.clinicaltrials.gov Unique Identifier: NCT0303612

Dapagliflozin across the range of ejection fraction in patients with heart failure: a patient-level, pooled meta-analysis of DAPA-HF and DELIVER

Whether the sodium–glucose cotransporter 2 inhibitor dapagliflozin reduces the risk of a range of morbidity and mortality outcomes in patients with heart failure regardless of ejection fraction is unknown. A patient-level pooled meta-analysis of two trials testing dapagliflozin in participants with heart failure and different ranges of left ventricular ejection fraction (≤40% and &gt;40%) was pre-specified to examine the effect of treatment on endpoints that neither trial, individually, was powered for and to test the consistency of the effect of dapagliflozin across the range of ejection fractions. The pre-specified endpoints were: death from cardiovascular causes; death from any cause; total hospital admissions for heart failure; and the composite of death from cardiovascular causes, myocardial infarction or stroke (major adverse cardiovascular events (MACEs)). A total of 11,007 participants with a mean ejection fraction of 44% (s.d. 14%) were included. Dapagliflozin reduced the risk of death from cardiovascular causes (hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.76–0.97; P = 0.01), death from any cause (HR 0.90, 95% CI 0.82–0.99; P = 0.03), total hospital admissions for heart failure (rate ratio 0.71, 95% CI 0.65–0.78; P &lt; 0.001) and MACEs (HR 0.90, 95% CI 0.81–1.00; P = 0.045). There was no evidence that the effect of dapagliflozin differed by ejection fraction. In a patient-level pooled meta-analysis covering the full range of ejection fractions in patients with heart failure, dapagliflozin reduced the risk of death from cardiovascular causes and hospital admissions for heart failure (PROSPERO: CRD42022346524)

Two problems of statistical estimation for stochastic processes

Le travail est consacré aux questions de la statistique des processus stochastiques. Particulièrement, on considère deux problèmes d'estimation. Le premier chapitre se concentre sur le problème d'estimation non-paramétrique pour le processus de Poisson non-homogène. On estime la fonction moyenne de ce processus, donc le problème est dans le domaine d'estimation non-paramétrique. On commence par la définition de l'efficacité asymptotique dans les problèmes non-paramétriques et on procède à exploration de l'existence des estimateurs asymptotiquement efficaces. On prend en considération la classe des estimateurs à noyau. Dans la thèse il est démontré que sous les conditions sur les coefficients du noyau par rapport à une base trigonométrique, on a l'efficacité asymptotique dans le sens minimax sur les ensembles divers. Les résultats obtenus soulignent le phénomène qu'en imposant des conditions de régularité sur la fonction inconnue, on peut élargir la classe des estimateurs asymptotiquement efficaces. Pour comparer les estimateurs asymptotiquement efficaces (du premier ordre), on démontre une inégalité qui nous permet de trouver un estimateur qui est asymptotiquement efficace du second ordre. On calcule aussi la vitesse de convergence pour cet estimateur, qui dépend de la régularité de la fonction inconnue et finalement on calcule la valeur minimale de la variance asymptotique pour cet estimateur. Cette valeur joue le même rôle dans l'estimation du second ordre que la constantede Pinsker dans le problème d'estimation de la densité ou encore l'information de Fisher dans les problèmes d'estimation paramétrique.Le deuxième chapitre est dédié au problème de l’estimation de la solution d’une équation différentielle stochastique rétrograde (EDSR). On observe un processus de diffusion qui est donnée par son équation différentielle stochastique dont le coefficient de la diffusion dépend d’un paramètre inconnu. Les observations sont discrètes. Pour estimer la solution de l’EDSR on a besoin d’un estimateur-processus pour leparamètre, qui, chaque instant n’utilise que la partie des observations disponible. Dans la littérature il existe une méthode de construction, qui minimise une fonctionnelle. On ne pouvait pas utiliser cet estimateur, car le calcul serait irréalisable. Dans le travail nous avons proposé un estimateur-processus qui a la forme simple et peut être facilement calculé. Cet estimateur-processus est un estimateur asymptotiquementefficace et en utilisant cet estimateur on estime la solution de l’EDSR de manière efficace aussi.This work is devoted to the questions of the statistics of stochastic processes. Particularly, the first chapter is devoted to a non-parametric estimation problem for an inhomogeneous Poisson process. The estimation problem is non-parametric due to the fact that we estimate the mean function. We start with the definition of the asymptotic efficiency in non-parametric estimation problems and continue with examination of the existence of asymptotically efficient estimators. We consider a class of kernel-type estimators. In the thesis we prove that under some conditions on the coefficients of the kernel with respect to a trigonometric basis we have asymptotic efficiency in minimax sense over various sets. The obtained results highlight the phenomenon that imposing regularity conditions on the unknown function, we can widen the class ofasymptotically efficient estimators. To compare these (first order) efficient estimators, we prove an inequality which allows us to find an estimator which is asymptotically efficient of second order. We calculate also the rate of convergence of this estimator, which depends on the regularity of the unknown function, and finally the minimal value of the asymptotic variance for this estimator is calculated. This value plays the same role in the second order estimation as the Pinsker constant in the density estimation problem or the Fisher information in parametric estimation problems. The second chapter is dedicated to a problem of estimation of the solution of a Backward Stochastic Differential Equation (BSDE). We observe a diffusion process which is given by its stochastic differential equation with the diffusion coefficientdepending on an unknown parameter. The observations are discrete. To estimate the solution of a BSDE, we need an estimator-process for a parameter, which, for each given time, uses only the available part of observations. In the literature there exists a method of construction, which minimizes a functional. We could not use this estimator, because the calculations would not be feasible. We propose an estimator-process which has a simple form and can be easily computed. Using this estimator we estimate the solution of a BSDE in an asymptotically efficient way

Deux problèmes d’estimation statistique pour les processus stochastiques

This work is devoted to the questions of the statistics of stochastic processes. Particularly, the first chapter is devoted to a non-parametric estimation problem for an inhomogeneous Poisson process. The estimation problem is non-parametric due to the fact that we estimate the mean function. We start with the definition of the asymptotic efficiency in non-parametric estimation problems and continue with examination of the existence of asymptotically efficient estimators. We consider a class of kernel-type estimators. In the thesis we prove that under some conditions on the coefficients of the kernel with respect to a trigonometric basis we have asymptotic efficiency in minimax sense over various sets. The obtained results highlight the phenomenon that imposing regularity conditions on the unknown function, we can widen the class ofasymptotically efficient estimators. To compare these (first order) efficient estimators, we prove an inequality which allows us to find an estimator which is asymptotically efficient of second order. We calculate also the rate of convergence of this estimator, which depends on the regularity of the unknown function, and finally the minimal value of the asymptotic variance for this estimator is calculated. This value plays the same role in the second order estimation as the Pinsker constant in the density estimation problem or the Fisher information in parametric estimation problems. The second chapter is dedicated to a problem of estimation of the solution of a Backward Stochastic Differential Equation (BSDE). We observe a diffusion process which is given by its stochastic differential equation with the diffusion coefficientdepending on an unknown parameter. The observations are discrete. To estimate the solution of a BSDE, we need an estimator-process for a parameter, which, for each given time, uses only the available part of observations. In the literature there exists a method of construction, which minimizes a functional. We could not use this estimator, because the calculations would not be feasible. We propose an estimator-process which has a simple form and can be easily computed. Using this estimator we estimate the solution of a BSDE in an asymptotically efficient way.Le travail est consacré aux questions de la statistique des processus stochastiques. Particulièrement, on considère deux problèmes d'estimation. Le premier chapitre se concentre sur le problème d'estimation non-paramétrique pour le processus de Poisson non-homogène. On estime la fonction moyenne de ce processus, donc le problème est dans le domaine d'estimation non-paramétrique. On commence par la définition de l'efficacité asymptotique dans les problèmes non-paramétriques et on procède à exploration de l'existence des estimateurs asymptotiquement efficaces. On prend en considération la classe des estimateurs à noyau. Dans la thèse il est démontré que sous les conditions sur les coefficients du noyau par rapport à une base trigonométrique, on a l'efficacité asymptotique dans le sens minimax sur les ensembles divers. Les résultats obtenus soulignent le phénomène qu'en imposant des conditions de régularité sur la fonction inconnue, on peut élargir la classe des estimateurs asymptotiquement efficaces. Pour comparer les estimateurs asymptotiquement efficaces (du premier ordre), on démontre une inégalité qui nous permet de trouver un estimateur qui est asymptotiquement efficace du second ordre. On calcule aussi la vitesse de convergence pour cet estimateur, qui dépend de la régularité de la fonction inconnue et finalement on calcule la valeur minimale de la variance asymptotique pour cet estimateur. Cette valeur joue le même rôle dans l'estimation du second ordre que la constantede Pinsker dans le problème d'estimation de la densité ou encore l'information de Fisher dans les problèmes d'estimation paramétrique.Le deuxième chapitre est dédié au problème de l’estimation de la solution d’une équation différentielle stochastique rétrograde (EDSR). On observe un processus de diffusion qui est donnée par son équation différentielle stochastique dont le coefficient de la diffusion dépend d’un paramètre inconnu. Les observations sont discrètes. Pour estimer la solution de l’EDSR on a besoin d’un estimateur-processus pour leparamètre, qui, chaque instant n’utilise que la partie des observations disponible. Dans la littérature il existe une méthode de construction, qui minimise une fonctionnelle. On ne pouvait pas utiliser cet estimateur, car le calcul serait irréalisable. Dans le travail nous avons proposé un estimateur-processus qui a la forme simple et peut être facilement calculé. Cet estimateur-processus est un estimateur asymptotiquementefficace et en utilisant cet estimateur on estime la solution de l’EDSR de manière efficace aussi

On Approximation of the BSDE with Unknown Volatility in Forward Equation

We consider the problem of the construction of the backward stochastic differential equation in the Markovian case. We suppose that the forward equation has a diffusion coefficient depending on some unknown parameter. We propose an estimator of this parameter constructed by the discrete time observations of the forward equation and then we use this estimator for approximation of the solution of the backward equation. The question of asymptotic optimality of this approximation is also discussed