958 resultados para split-plot designs


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BACKGROUND: The past three decades have seen rapid improvements in the diagnosis and treatment of most cancers and the most important contributor has been research. Progress in rare cancers has been slower, not least because of the challenges of undertaking research. SETTINGS: The International Rare Cancers Initiative (IRCI) is a partnership which aims to stimulate and facilitate the development of international clinical trials for patients with rare cancers. It is focused on interventional--usually randomized--clinical trials with the clear goal of improving outcomes for patients. The key challenges are organisational and methodological. A multi-disciplinary workshop to review the methods used in ICRI portfolio trials was held in Amsterdam in September 2013. Other as-yet unrealised methods were also discussed. RESULTS: The IRCI trials are each presented to exemplify possible approaches to designing credible trials in rare cancers. Researchers may consider these for use in future trials and understand the choices made for each design. INTERPRETATION: Trials can be designed using a wide array of possibilities. There is no 'one size fits all' solution. In order to make progress in the rare diseases, decisions to change practice will have to be based on less direct evidence from clinical trials than in more common diseases.

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This study deals with the statistical properties of a randomization test applied to an ABAB design in cases where the desirable random assignment of the points of change in phase is not possible. In order to obtain information about each possible data division we carried out a conditional Monte Carlo simulation with 100,000 samples for each systematically chosen triplet. Robustness and power are studied under several experimental conditions: different autocorrelation levels and different effect sizes, as well as different phase lengths determined by the points of change. Type I error rates were distorted by the presence of autocorrelation for the majority of data divisions. Satisfactory Type II error rates were obtained only for large treatment effects. The relationship between the lengths of the four phases appeared to be an important factor for the robustness and the power of the randomization test.

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N = 1 designs imply repeated registrations of the behaviour of the same experimental unit and the measurements obtained are often few due to time limitations, while they are also likely to be sequentially dependent. The analytical techniques needed to enhance statistical and clinical decision making have to deal with these problems. Different procedures for analysing data from single-case AB designs are discussed, presenting their main features and revising the results reported by previous studies. Randomization tests represent one of the statistical methods that seemed to perform well in terms of controlling false alarm rates. In the experimental part of the study a new simulation approach is used to test the performance of randomization tests and the results suggest that the technique is not always robust against the violation of the independence assumption. Moreover, sensitivity proved to be generally unacceptably low for series lengths equal to 30 and 40. Considering the evidence available, there does not seem to be an optimal technique for single-case data analysis

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The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

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Monte Carlo simulations were used to generate data for ABAB designs of different lengths. The points of change in phase are randomly determined before gathering behaviour measurements, which allows the use of a randomization test as an analytic technique. Data simulation and analysis can be based either on data-division-specific or on common distributions. Following one method or another affects the results obtained after the randomization test has been applied. Therefore, the goal of the study was to examine these effects in more detail. The discrepancies in these approaches are obvious when data with zero treatment effect are considered and such approaches have implications for statistical power studies. Data-division-specific distributions provide more detailed information about the performance of the statistical technique.

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PURPOSE: Congenital hypogonadotropic hypogonadism (CHH) and split hand/foot malformation (SHFM) are two rare genetic conditions. Here we report a clinical entity comprising the two. METHODS: We identified patients with CHH and SHFM through international collaboration. Probands and available family members underwent phenotyping and screening for FGFR1 mutations. The impact of identified mutations was assessed by sequence- and structure-based predictions and/or functional assays. RESULTS: We identified eight probands with CHH with (n = 3; Kallmann syndrome) or without anosmia (n = 5) and SHFM, seven of whom (88%) harbor FGFR1 mutations. Of these seven, one individual is homozygous for p.V429E and six individuals are heterozygous for p.G348R, p.G485R, p.Q594*, p.E670A, p.V688L, or p.L712P. All mutations were predicted by in silico analysis to cause loss of function. Probands with FGFR1 mutations have severe gonadotropin-releasing hormone deficiency (absent puberty and/or cryptorchidism and/or micropenis). SHFM in both hands and feet was observed only in the patient with the homozygous p.V429E mutation; V429 maps to the fibroblast growth factor receptor substrate 2α binding domain of FGFR1, and functional studies of the p.V429E mutation demonstrated that it decreased recruitment and phosphorylation of fibroblast growth factor receptor substrate 2α to FGFR1, thereby resulting in reduced mitogen-activated protein kinase signaling. CONCLUSION: FGFR1 should be prioritized for genetic testing in patients with CHH and SHFM because the likelihood of a mutation increases from 10% in the general CHH population to 88% in these patients.Genet Med 17 8, 651-659.

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JXTA is an open peer-to-peer (P2P) protocols specification that, in its about 10 years of history, has slowly evolved to appeal to a broad set of applications. As part of this process,some long awaited security improvements have been included in the latest versions. However, under some contexts, even more advanced security requirements should be met, such as anonymity. Several approaches exist to deploy anonymity in P2P networks, but no perfect solution exists. Even though path-based approaches are quite popular, it is considered that, in dynamicgroups, using a split message-based one is better. In this work, we propose an anonymity service for JXTA using such approach. The proposal takes advantage JXTA's core services, in a manner so that it can be easily integrated to existing end applications and services.

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One of the problems with books which are relatively general in nature is that many of the individual contributions tend to be so narrow and specialised that only the author has any knowledge of (or interest in) the issues under discussion. At first sight this appears to be the case with Detecting Detection. Fortunately, however, first impressions are deceiving. Although the essays in the volume deal with writers as diverse and disparate as the Catalano-Spanish writer Juan Marse, the Bulgarian-French philosopher Julia Kristeva and the once-vaunted giant of English literature,Graham Greene, among numerous others, there is much to be enjoyed and learnt, even if some of the works under discussion are unfamiliar to the crime fiction reader and/or scholar to whom the book initially appears to be directed.

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The present study builds on a previous proposal for assigning probabilities to the outcomes computed using different primary indicators in single-case studies. These probabilities are obtained comparing the outcome to previously tabulated reference values and reflect the likelihood of the results in case there was no intervention effect. The current study explores how well different metrics are translated into p values in the context of simulation data. Furthermore, two published multiple baseline data sets are used to illustrate how well the probabilities could reflect the intervention effectiveness as assessed by the original authors. Finally, the importance of which primary indicator is used in each data set to be integrated is explored; two ways of combining probabilities are used: a weighted average and a binomial test. The results indicate that the translation into p values works well for the two nonoverlap procedures, with the results for the regression-based procedure diverging due to some undesirable features of its performance. These p values, both when taken individually and when combined, were well-aligned with the effectiveness for the real-life data. The results suggest that assigning probabilities can be useful for translating the primary measure into the same metric, using these probabilities as additional evidence on the importance of behavioral change, complementing visual analysis and professional's judgments.

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The optimization of the anaerobic degradation of the azo dye Remazol golden yellow RNL was performed according to multivariate experimental designs: a 2² full-factorial design and a central composite design (CCD). The CCD revealed that the best incubation conditions (90% color removal) for the degradation of the azo dye (50 mg L- 1) were achieved with 350 mg L- 1 of yeast extract and 45 mL of anaerobic supernatant (free cell extract) produced from the incubation of 650 mg L- 1 of anaerobic microorganisms and 250 mg L- 1 of glucose. A first-order kinetics model best fit the experimental data (k = 0.0837 h- 1, R² = 0.9263).

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Software plays an important role in our society and economy. Software development is an intricate process, and it comprises many different tasks: gathering requirements, designing new solutions that fulfill these requirements, as well as implementing these designs using a programming language into a working system. As a consequence, the development of high quality software is a core problem in software engineering. This thesis focuses on the validation of software designs. The issue of the analysis of designs is of great importance, since errors originating from designs may appear in the final system. It is considered economical to rectify the problems as early in the software development process as possible. Practitioners often create and visualize designs using modeling languages, one of the more popular being the Uni ed Modeling Language (UML). The analysis of the designs can be done manually, but in case of large systems, the need of mechanisms that automatically analyze these designs arises. In this thesis, we propose an automatic approach to analyze UML based designs using logic reasoners. This approach firstly proposes the translations of the UML based designs into a language understandable by reasoners in the form of logic facts, and secondly shows how to use the logic reasoners to infer the logical consequences of these logic facts. We have implemented the proposed translations in the form of a tool that can be used with any standard compliant UML modeling tool. Moreover, we authenticate the proposed approach by automatically validating hundreds of UML based designs that consist of thousands of model elements available in an online model repository. The proposed approach is limited in scope, but is fully automatic and does not require any expertise of logic languages from the user. We exemplify the proposed approach with two applications, which include the validation of domain specific languages and the validation of web service interfaces.

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Assessing the growth and floristic composition of species that grow under the canopy of trees is important for weed control (WC). The objective of this study was to assess two experiments (E1 and E2), when the trees were two years and one year of age, respectively. In E1, sabiá (S) and gliricidia (G) were submitted to planting densities from 400 to 1.200 plants ha-1. In E2, growing systems consisting of S, G, and neem (N) combinations were compared: SSS, GGG, NNN, GSG, NSN, SGS, NGN, SNS, and GNG (each letter represents a row of plants). A random block design was adopted, with three (E1) and four (E2) replicates. In E1, treatments were arranged as split-plots (species in plots). In E2, the degrees of freedom for treatments (8) were partitioned into growing systems (treatments that involved the same species) and between growing system groups (2). Twenty-one weed species were found in E1. Gliricidia attained greater plant height than sabiá, but these species did not differ in canopy diameter, number of weed species per plot, and weed green and dry biomass of the shoot. Higher planting densities resulted in the reduction of all those traits. Twenty-six weed species were found in E2. Growing systems that included gliricidia showed canopies with greater diameters than growing systems that included neem. There were no differences between growing systems for number of weed species per plot and for weed green and dry biomass of the shoot.