902 resultados para Incidental parameter bias


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OBJECTIVE: The aim of this study was to examine the differences between those who gave informed consent to a study on substance use and those who did not, and to analyze whether differences changed with varying nonconsent rates. METHOD: Cross-sectional questionnaire data on demographics, alcohol, smoking, and cannabis use were obtained for 6,099 French- and 5,720 German-speaking 20-year-old Swiss men. Enrollment took place over 11 months for the Cohort Study on Substance Use Risk Factors (C-SURF). Consenters and nonconsenters were asked to complete a short questionnaire. Data for nearly the entire population were available because 94% responded. Weekly differences in consent rates were analyzed. Regressions examined the associations of substance use with consent giving and consent rates and the interaction between the two. RESULTS: Nonconsenters had higher substance use patterns, although they were more often alcohol abstainers; differences were small and not always significant and did not decrease as consent rates increased. CONCLUSIONS: Substance use currently is a minor sensitive topic among young men, resulting in small differences between nonconsenters and consenters. As consent rates increase, additional individuals are similar to those observed at lower consent rates. Estimates of analytical studies looking at associations of substance use with other variables will not differ at reasonable consent rates of 50%-80%. Descriptive prevalence studies may be biased, but only at very low rates of consent.

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Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

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MOTIVATION: Comparative analyses of gene expression data from different species have become an important component of the study of molecular evolution. Thus methods are needed to estimate evolutionary distances between expression profiles, as well as a neutral reference to estimate selective pressure. Divergence between expression profiles of homologous genes is often calculated with Pearson's or Euclidean distance. Neutral divergence is usually inferred from randomized data. Despite being widely used, neither of these two steps has been well studied. Here, we analyze these methods formally and on real data, highlight their limitations and propose improvements. RESULTS: It has been demonstrated that Pearson's distance, in contrast to Euclidean distance, leads to underestimation of the expression similarity between homologous genes with a conserved uniform pattern of expression. Here, we first extend this study to genes with conserved, but specific pattern of expression. Surprisingly, we find that both Pearson's and Euclidean distances used as a measure of expression similarity between genes depend on the expression specificity of those genes. We also show that the Euclidean distance depends strongly on data normalization. Next, we show that the randomization procedure that is widely used to estimate the rate of neutral evolution is biased when broadly expressed genes are abundant in the data. To overcome this problem, we propose a novel randomization procedure that is unbiased with respect to expression profiles present in the datasets. Applying our method to the mouse and human gene expression data suggests significant gene expression conservation between these species. CONTACT: marc.robinson-rechavi@unil.ch; sven.bergmann@unil.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

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Several observations support the hypothesis that differences in synaptic and regional cerebral plasticity between the sexes account for the high ratio of males to females in autism. First, males are more susceptible than females to perturbations in genes involved in synaptic plasticity. Second, sex-related differences in non-autistic brain structure and function are observed in highly variable regions, namely, the heteromodal associative cortices, and overlap with structural particularities and enhanced activity of perceptual associative regions in autistic individuals. Finally, functional cortical reallocations following brain lesions in non-autistic adults (for example, traumatic brain injury, multiple sclerosis) are sex-dependent. Interactions between genetic sex and hormones may therefore result in higher synaptic and consecutively regional plasticity in perceptual brain areas in males than in females. The onset of autism may largely involve mutations altering synaptic plasticity that create a plastic reaction affecting the most variable and sexually dimorphic brain regions. The sex ratio bias in autism may arise because males have a lower threshold than females for the development of this plastic reaction following a genetic or environmental event.

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Geophysical data may provide crucial information about hydrological properties, states, and processes that are difficult to obtain by other means. Large data sets can be acquired over widely different scales in a minimally invasive manner and at comparatively low costs, but their effective use in hydrology makes it necessary to understand the fidelity of geophysical models, the assumptions made in their construction, and the links between geophysical and hydrological properties. Geophysics has been applied for groundwater prospecting for almost a century, but it is only in the last 20 years that it is regularly used together with classical hydrological data to build predictive hydrological models. A largely unexplored venue for future work is to use geophysical data to falsify or rank competing conceptual hydrological models. A promising cornerstone for such a model selection strategy is the Bayes factor, but it can only be calculated reliably when considering the main sources of uncertainty throughout the hydrogeophysical parameter estimation process. Most classical geophysical imaging tools tend to favor models with smoothly varying property fields that are at odds with most conceptual hydrological models of interest. It is thus necessary to account for this bias or use alternative approaches in which proposed conceptual models are honored at all steps in the model building process.

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BACKGROUND: Best corrected visual acuity (BCVA) of 0.8 or above in AMD patients can sometimes correspond to poor macular function inducing a serious visual handicap. Microperimetry can be used to objectivize this difference. PATIENTS AND METHODS: A retrospective study was undertaken on 233 files of AMD patients of whom 82 had had a microperimetry. BCVA was compared with microperimetry performance. All examinations were performed in an identical setting by the same team of 3 persons. RESULTS: Among the 82 patients included, 32 (39.0%) had a BCVA equal to or above 0.8 even though their microperimetry performance was lower than 200/560 db. 10 of them (12.2% of total) had an even poorer microperimetry below 120/560 db indicating poor macular function. CONCLUSIONS: More than a third of the AMD patients had a bad or very bad microperimetry performance in parallel with a good visual acuity. Microperimetry is a valuable tool to assess and follow real macular function in AMD patients when visual acuity alone can be misleading.

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Linfangiectasia renal é uma condição rara, caracterizada por coleções parapiélicas e perirrenais, que pode progredir de assintomática para insuficiência renal crônica. É apresentado um caso de achado incidental em tomografia computadorizada de linfangiectasia renal bilateral em paciente assintomático, com descrição dos principais achados à luz dos métodos de imagem e ampla revisão da literatura.

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Objective To evaluate intra- and interobserver agreement in the identification of incidental enchondromas at knee magnetic resonance imaging, and to assess the prevalence of imaging findings. Materials and Methods Retrospective study reviewing 326 knee magnetic resonance images acquired in the period between November 2009 and September 2010. The images were independently and blindly analyzed by two specialists in musculoskeletal radiology, with the objective of identifying incidental enchondromas, presence of foci with signal similar to bone marrow and foci of signal absence suggestive of calcifications within the enchondromas. Inter- and intraobserver agreements were analyzed. Results Eleven lesions compatible with enchondromas (3.3%) were identified. The interobserver agreement for the presence of enchondroma was high. Prevalence of foci of bone marrow signal inside the enchondromas was of 54.55%, and foci suggestive of calcification corresponded to 36.36%. The intraobserver agreement for foci of bone marrow signal in enchondromas was perfect, and interobserver agreement was high. Conclusion The prevalence of incidental enchondromas in the current study was compatible with data in the literature. Excellent agreement was observed in the identification of enchondromas and in the assessment of imaging findings. A higher prevalence of fat signal foci was observed as compared with signal absence suggestive of calcifications.