Composite time-to-event endpoints are commonly used in cardiovascular outcome trials. For example, the IMPROVE-IT trial comparing ezetimibe+simvastatin to placebo+simvastatin in 18,144 patients with ...acute coronary syndrome used a primary composite endpoint with five component outcomes: (1) cardiovascular death, (2) non-fatal stroke, (3) non-fatal myocardial infarction, (4) coronary revascularization ≥30 days after randomization, and (5) unstable angina requiring hospitalization. In such settings, the traditional analysis compares treatments using the observed time to the occurrence of the first (i.e. earliest) component outcome for each patient. This approach ignores information for subsequent outcome(s), possibly leading to reduced power to demonstrate the benefit of the test versus the control treatment. We use real data examples and simulations to contrast the traditional approach with several alternative approaches that use data for all the intra-patient component outcomes, not just the first.
Non-proportional hazards data are routinely encountered in randomized clinical trials. In such cases, classic Cox proportional hazards model can suffer from severe power loss, with difficulty in ...interpretation of the estimated hazard ratio since the treatment effect varies over time. We propose CauchyCP, an omnibus test of change-point Cox regression models, to overcome both challenges while detecting signals of non-proportional hazards patterns. Extensive simulation studies demonstrate that, compared to existing treatment comparison tests under non-proportional hazards, the proposed CauchyCP test (a) controls the type I error better at small
α
levels (
<
0.01
); (b) increases the power of detecting time-varying effects; and (c) is more computationally efficient than popular methods like MaxCombo for large-scale data analysis. The superior performance of CauchyCP is further illustrated using retrospective analyses of two randomized clinical trial datasets and a pharmacogenetic biomarker study dataset. The R package CauchyCP is publicly available on CRAN.
In some randomized (drug versus placebo) clinical trials, the estimand of interest is the between-treatment difference in population means of a clinical endpoint that is free from the confounding ...effects of "rescue" medication (e.g., HbA1c change from baseline at 24 weeks that would be observed without rescue medication regardless of whether or when the assigned treatment was discontinued). In such settings, a missing data problem arises if some patients prematurely discontinue from the trial or initiate rescue medication while in the trial, the latter necessitating the discarding of post-rescue data. We caution that the commonly used mixed-effects model repeated measures analysis with the embedded missing at random assumption can deliver an exaggerated estimate of the aforementioned estimand of interest. This happens, in part, due to implicit imputation of an overly optimistic mean for "dropouts" (i.e., patients with missing endpoint data of interest) in the drug arm. We propose an alternative approach in which the missing mean for the drug arm dropouts is explicitly replaced with either the estimated mean of the entire endpoint distribution under placebo (primary analysis) or a sequence of increasingly more conservative means within a tipping point framework (sensitivity analysis); patient-level imputation is not required. A supplemental "dropout = failure" analysis is considered in which a common poor outcome is imputed for all dropouts followed by a between-treatment comparison using quantile regression. All analyses address the same estimand and can adjust for baseline covariates. Three examples and simulation results are used to support our recommendations.
In this work, we have developed a model to describe the behavior of liquid drops upon impaction on hydrophobic particle bed and verified it experimentally. Poly(tetrafluoroethylene) (PTFE) particles ...were used to coat drops of water, aqueous solutions of glycerol (20, 40, and 60% v/v), and ethanol (5 and 12% v/v). The experiments were conducted for Weber number (We) ranging from 8 to 130 and Reynolds number (Re) ranging from 370 to 4460. The bed porosity was varied from 0.8 to 0.6. The experimental values of βmax (ratio of the diameter at the maximum spreading condition to the initial drop diameter) were estimated from the time-lapsed images captured using a high-speed camera. The theoretical βmax was estimated by making energy balances on the liquid drop. The proposed model accounts for the energy losses due to viscous dissipation and crater formation along with a change in kinetic energy and surface energy. A good agreement was obtained between the experimental βmax and the estimated theoretical βmax. The proposed model yielded a least % absolute average relative deviation (% AARD) of 5.5 ± 4.3 compared to other models available in the literature. Further, it was found that the liquid drops impacting on particle bed are completely coated with PTFE particles with βmax values greater than 2.
•A more efficient solid state method is proposed for synthesizing quasi one-dimensional BaVSe3.•The thermoelectric properties of BaVSe3 at low temperatures is reportedfor the first time.•The presence ...of cluster glass nature and Griffith-like phase is detected in the pure phase BaVSe3 system.•The BaVSe3 system demonstrates butterfly magnetoresistance (BMR) at extremely low temperatures.
Quasi-one-dimensional (Q1D) materials possess extraordinary magnetic and transport characteristics, which render them of critical importance. In this study, we present the synthesis of phase pure Q1D BaVSe3 via a novel heat treatment process and analyze its low-temperature thermoelectric, magnetotransport, and magnetic properties. A comparison is made between this phase pure system and the BaVSe3 infused with the secondary BaSe3 phase that we previously reported todetermine the potential effect of impurities on the magnetic and thermoelectric properties. Our previous study covered the experimental and theoretical investigation on the exotic magnetic properties of BaVSe3 with BaSe3. In phase pure BaVSe3, at TC ∼ 41.2 K, a paramagnetic to ferromagnetic transition with a magnetic moment of 1.74 μB from V4+ was observed,consistent with calculated values. At very low temperatures (<117 K), the material behaved like glass, showed Griffiths-like behaviorand spin valve-like butterfly magnetoresistance (BMR). Experiments indicate that these exotic magnetic properties are caused by the interaction between the A1g and t2g levels of the V 3d orbital and their spin orientation. Magnetic frustration in these materials is caused by V-V inter and intra chain interactions in the 1D spin chains of V. Q1D BaVSe3 (BVS) demonstrated n-type thermoelectricity with a thermoelectric figure of merit of ∼ 0.008, 4 × greater than BaVSe3:BaSe3 (BVS: BS) at room temperature. The thermal transport is determined to be primarily phonon-dependent. The magnetic property studies disclosed that this FM transition metal chalcogenide can contribute to low-temperature spintronics.
Erbium (Er3+) substituted nanocrystalline, cobalt-rich ferrites, which can be represented chemically as Co1.1Fe1.9–x Er x O4 (CFEO; x = 0.0–0.2), were synthesized by the sol–gel autocombustion ...method. The structural, dielectric, and electrical transport properties of CFEO were investigated in detail. CFEO materials crystallize in a spinel cubic structure for x ≤ 0.10; formation of orthoferrite (ErFeO3) secondary phase was noted for x ≥ 0.15. Microstructural and compositional studies revealed the formation of spherical, elongated grains with stoichiometric presence of Co, Fe, Er, and O. The dielectric constant (ε′) dispersion fits to the Debye’s function for all CFEO ceramics. The relaxation time and spread factor obtained from ε′ dispersion are ∼10–3 s and ∼0.50 (±0.10), respectively. The complex impedance analyses confirm a grain-interior mechanism contributing to the dielectric properties. Semiconducting behavior and small polaron conduction mechanism were evident in electrical transport properties of CFEO materials.
In the second half of 2014, the Steering Committee of the International Council for Harmonisation endorsed the formation of an expert working group to develop an addendum to the International Council ...for Harmonisation E9 guideline (Statistical Principles for Clinical Trials). The addendum was to focus on two clinical trial topics: estimands and sensitivity analysis. A draft of the addendum, referred to as E9/R1, was developed by the expert working group and made available for public comments across the International Council for Harmonisation regions in the second half of 2017. A structured framework for clinical trial design and analysis proposed in the draft addendum are briefly described, including four key inputs for developing objective-driven estimands and strategies for tackling one of the inputs (‘intercurrent events’). The proposed framework aligns each clinical trial objective with the corresponding statistical target of estimation (estimand), trial design and data to be collected, main method of estimation/inference, and sensitivity analysis to pressure test key analytic assumption(s) in the main analysis. A case study from the diabetes therapeutic area illustrates how the framework can be implemented in practice. International Council for Harmonisation E9/R1 is expected to enable better planning, conduct, analysis, and interpretation of randomised clinical trials. This will facilitate improvements in new drug applications and strengthen understanding of decision making by regulatory authorities and advisory committees.
In the era of precision medicine, many biomarkers have been discovered to be associated with drug efficacy and safety responses, which can be used for patient stratification and drug response ...prediction. Due to the small sample size and limited power of randomized clinical studies, meta-analysis is usually conducted to aggregate all available studies to maximize the power for identifying prognostic and predictive biomarkers. However, it is often challenging to find an independent study to replicate the discoveries from the meta-analysis (e.g. meta-analysis of pharmacogenomics genome-wide association studies (PGx GWAS)), which seriously limits the potential impacts of the discovered biomarkers. To overcome this challenge, we develop a novel statistical framework, MAJAR (meta-analysis of joint effect associations for biomarker replicability assessment), to jointly test prognostic and predictive effects and assess the replicability of identified biomarkers by implementing an enhanced expectation–maximization algorithm and calculating their posterior-probability-of-replicabilities and Bayesian false discovery rates (Fdr). Extensive simulation studies were conducted to compare the performance of MAJAR and existing methods in terms of Fdr, power, and computational efficiency. The simulation results showed improved statistical power with well-controlled Fdr of MAJAR over existing methods and robustness to outliers under different data generation processes. We further demonstrated the advantages of MAJAR over existing methods by applying MAJAR to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial. Compared to testing main effects only, MAJAR identified 12 novel variants associated with the treatment-related low-density lipoprotein cholesterol reduction from baseline.
Atmospheric aerosol, particulate matter suspended in the air we breathe, exerts a strong impact on our health and the environment. Controlling the amount of particulate matter in air is difficult, as ...there are many ways particles can form by both natural and anthropogenic processes. We gain insight into the sources of particulate matter through chemical composition measurements. A substantial portion of atmospheric aerosol is organic, and this organic matter is exceedingly complex on a molecular scale, encompassing hundreds to thousands of individual compounds that distribute between the gas and particle phases. Because of this complexity, no single analytical technique is sufficient. However, mass spectrometry plays a crucial role owing to its combination of high sensitivity and molecular specificity. This review surveys the various ways mass spectrometry is used to characterize atmospheric organic aerosol at a molecular level, tracing these methods from inception to current practice, with emphasis on current and emerging areas of research. Both offline and online approaches are covered, and molecular measurements with them are discussed in the context of identifying sources and elucidating the underlying chemical mechanisms of particle formation. There is an ongoing need to improve existing techniques and develop new ones if we are to further advance our knowledge of how to mitigate the unwanted health and environmental impacts of particles.
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