795 resultados para Trials (Breach of promise)
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Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data. Many of the issues that are discussed with reference to the statistical analysis of compositional data have a natural counterpart in the construction of a Bayesian statistical model for categorical data. This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986) in his seminal book on compositional data. Particular emphasis is put on the problem of what parameterization to use
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Monográfico con el título: 'Pedagogía crítica del S. XXI'. Resumen basado en el de la publicación
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n this new CEPS commentary, CEPS Director Daniel Gros takes a closer look at the US experience to point out that the federal budget provides much less insurance against state specific shocks than widely assumed, while the US Banking Union act as a very powerful shock absorber. Accordingly, he argues that the euro’s long-term stability depends far more on completing plans for a European banking union than on the introduction of a fiscal capacity for the eurozone.
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The uptake of arsenic (As) by plants from contaminated soils presents a health hazard that may affect the use of agricultural and former industrial land. Methods for limiting the hazard are desirable. A proposed remediation treatment comprises the precipitation of iron (Fe) oxides in the contaminated soil by adding ferrous sulfate and lime. The effects on As bioavailability were assessed using a range of vegetable crops grown in the field. Four UK locations were used, where soil was contaminated by As from different sources. At the most contaminated site, a clay loam containing a mean of 748 mg As kg(-1) soil, beetroot, calabrese, cauliflower, lettuce, potato, radish and spinach were grown. For all crops except spinach, ferrous sulfate treatment caused a significant reduction in the bioavailability of As in some part of the crop. Application of ferrous sulfate in solution, providing 0.2% Fe oxides in the soil (0-10 cm), reduced As uptake by a mean of 22%. Solid ferrous sulfate was applied to give concentrations of 0.5% and 1% Fe oxides: the 0.5% concentration reduced As uptake by a mean of 32% and the 1% concentration gave no significant additional benefit. On a sandy loam containing 65 mg As kg(-1) soil, there was tentative evidence that ferrous sulfate treatment up to 2% Fe oxides caused a significant reduction in lettuce As, but calabrese did not respond. At the other two sites, the effects of ferrous sulfate treatment were not significant, but the uptake of soil As was low in treated and untreated soils. Differences between sites in the bioavailable fraction of soil As may be related to the soil texture or the source of As. The highest bioavailability was found on the soil which had been contaminated by aerial deposition and had a high sand content. (C) 2003 Elsevier Science B.V. All rights reserved.
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Effective use and recycling of manures together with occasional and judicious use of supplementary fertilizing materials forms the basis for management of phosphorus (P) and potassium (K) within organic farming systems. Replicated field trials were established at three sites across the UK to compare the supply of P and K to grass-clover swards cut for silage from a range of fertilizing materials, and to assess the usefulness of routine soil tests for P and K in organic farming systems. None of the fertilizing materials (farmyard manure, rock phosphate, Kali vinasse, volcanic tuff) significantly increased silage yields, nor was P offtake increased. However, farmyard manure and Kali vinasse proved effective sources of K to grass and clover in the short to medium term. Available P (measured as Olsen-P) showed no clear relationship with crop P offtake in these trials. In contrast, available K (measured by ammonium nitrate extraction) proved a useful measurement to predict K availability to crops and support K management decisions.
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The uptake of arsenic (As) by plants from contaminated soils presents a health hazard that may affect the use of agricultural and former industrial land. Methods for limiting the hazard are desirable. A proposed remediation treatment comprises the precipitation of iron (Fe) oxides in the contaminated soil by adding ferrous sulfate and lime. The effects on As bioavailability were assessed using a range of vegetable crops grown in the field. Four UK locations were used, where soil was contaminated by As from different sources. At the most contaminated site, a clay loam containing a mean of 748 mg As kg(-1) soil, beetroot, calabrese, cauliflower, lettuce, potato, radish and spinach were grown. For all crops except spinach, ferrous sulfate treatment caused a significant reduction in the bioavailability of As in some part of the crop. Application of ferrous sulfate in solution, providing 0.2% Fe oxides in the soil (0-10 cm), reduced As uptake by a mean of 22%. Solid ferrous sulfate was applied to give concentrations of 0.5% and 1% Fe oxides: the 0.5% concentration reduced As uptake by a mean of 32% and the 1% concentration gave no significant additional benefit. On a sandy loam containing 65 mg As kg(-1) soil, there was tentative evidence that ferrous sulfate treatment up to 2% Fe oxides caused a significant reduction in lettuce As, but calabrese did not respond. At the other two sites, the effects of ferrous sulfate treatment were not significant, but the uptake of soil As was low in treated and untreated soils. Differences between sites in the bioavailable fraction of soil As may be related to the soil texture or the source of As. The highest bioavailability was found on the soil which had been contaminated by aerial deposition and had a high sand content. (C) 2003 Elsevier Science B.V. All rights reserved.
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This paper reviews state-of-art statistical designs for dose-escalation procedures in first-into-man studies. The main focus will be on studies in oncology, as most statistical procedures for phase I trials have been proposed in this context. Extensions to situations such as the observation of bivariate outcomes and healthy volunteer studies are also discussed. The number of dose levels and cohort sizes used in early phase trials are considered. Finally, this paper raises some practical issues for dose-escalation procedures.
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Background: The objective was to evaluate the efficacy and tolerability of donepezil (5 and 10 mg/day) compared with placebo in alleviating manifestations of mild to moderate Alzheimer's disease (AD). Method: A systematic review of individual patient data from Phase II and III double-blind, randomised, placebo-controlled studies of up to 24 weeks and completed by 20 December 1999. The main outcome measures were the ADAS-cog, the CIBIC-plus, and reports of adverse events. Results: A total of 2376 patients from ten trials were randomised to either donepezil 5 mg/day (n = 821), 10 mg/day (n = 662) or placebo (n = 893). Cognitive performance was better in patients receiving donepezil than in patients receiving placebo. At 12 weeks the differences in ADAS-cog scores were 5 mg/day-placebo: - 2.1 [95% confidence interval (CI), - 2.6 to - 1.6; p < 0.001], 10 mg/day-placebo: - 2.5 ( - 3.1 to - 2.0; p < 0.001). The corresponding results at 24 weeks were - 2.0 ( - 2.7 to - 1.3; p < 0.001) and - 3.1 ( - 3.9 to - 2.4; p < 0.001). The difference between the 5 and 10 mg/day doses was significant at 24 weeks (p = 0.005). The odds ratios (OR) of improvement on the CIBIC-plus at 12 weeks were: 5 mg/day-placebo 1.8 (1.5 to 2.1; p < 0.001), 10 mg/day-placebo 1.9 (1.5 to 2.4; p < 0.001). The corresponding values at 24 weeks were 1.9 (1.5 to 2.4; p = 0.001) and 2.1 (1.6 to 2.8; p < 0.001). Donepezil was well tolerated; adverse events were cholinergic in nature and generally of mild severity and brief in duration. Conclusion: Donepezil (5 and 10 mg/day) provides meaningful benefits in alleviating deficits in cognitive and clinician-rated global function in AD patients relative to placebo. Increased improvements in cognition were indicated for the higher dose. Copyright © 2004 John Wiley & Sons, Ltd.
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Background: Meta-analyses based on individual patient data (IPD) are regarded as the gold standard for systematic reviews. However, the methods used for analysing and presenting results from IPD meta-analyses have received little discussion. Methods We review 44 IPD meta-analyses published during the years 1999–2001. We summarize whether they obtained all the data they sought, what types of approaches were used in the analysis, including assumptions of common or random effects, and how they examined the effects of covariates. Results: Twenty-four out of 44 analyses focused on time-to-event outcomes, and most analyses (28) estimated treatment effects within each trial and then combined the results assuming a common treatment effect across trials. Three analyses failed to stratify by trial, analysing the data is if they came from a single mega-trial. Only nine analyses used random effects methods. Covariate-treatment interactions were generally investigated by subgrouping patients. Seven of the meta-analyses included data from less than 80% of the randomized patients sought, but did not address the resulting potential biases. Conclusions: Although IPD meta-analyses have many advantages in assessing the effects of health care, there are several aspects that could be further developed to make fuller use of the potential of these time-consuming projects. In particular, IPD could be used to more fully investigate the influence of covariates on heterogeneity of treatment effects, both within and between trials. The impact of heterogeneity, or use of random effects, are seldom discussed. There is thus considerable scope for enhancing the methods of analysis and presentation of IPD meta-analysis.
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Pharmacogenetic trials investigate the effect of genotype on treatment response. When there are two or more treatment groups and two or more genetic groups, investigation of gene-treatment interactions is of key interest. However, calculation of the power to detect such interactions is complicated because this depends not only on the treatment effect size within each genetic group, but also on the number of genetic groups, the size of each genetic group, and the type of genetic effect that is both present and tested for. The scale chosen to measure the magnitude of an interaction can also be problematic, especially for the binary case. Elston et al. proposed a test for detecting the presence of gene-treatment interactions for binary responses, and gave appropriate power calculations. This paper shows how the same approach can also be used for normally distributed responses. We also propose a method for analysing and performing sample size calculations based on a generalized linear model (GLM) approach. The power of the Elston et al. and GLM approaches are compared for the binary and normal case using several illustrative examples. While more sensitive to errors in model specification than the Elston et al. approach, the GLM approach is much more flexible and in many cases more powerful. Copyright © 2005 John Wiley & Sons, Ltd.
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This paper considers methods for testing for superiority or non-inferiority in active-control trials with binary data, when the relative treatment effect is expressed as an odds ratio. Three asymptotic tests for the log-odds ratio based on the unconditional binary likelihood are presented, namely the likelihood ratio, Wald and score tests. All three tests can be implemented straightforwardly in standard statistical software packages, as can the corresponding confidence intervals. Simulations indicate that the three alternatives are similar in terms of the Type I error, with values close to the nominal level. However, when the non-inferiority margin becomes large, the score test slightly exceeds the nominal level. In general, the highest power is obtained from the score test, although all three tests are similar and the observed differences in power are not of practical importance. Copyright (C) 2007 John Wiley & Sons, Ltd.
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The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.
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We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.