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Biometry --- Biometry --- Data processing. --- Research.
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Biometry --- Crop science
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In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years.
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Biometry --- Biometry. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Statistical methods --- Biomathematics --- Statistics
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Biometric identification --- Biometry --- Biométrie --- Biometry. --- Périodiques. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Statistical methods --- Biomathematics --- Statistics --- Periodicals.
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This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.
Medical statistics --- Biometry --- Data Interpretation, Statistical --- Models, Statistical --- Data processing --- Medical statistics. --- Biometry. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Health --- Health statistics --- Medicine --- Statistical methods --- Data processing. --- Medical statistics - Data processing --- Biometry - Data processing
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Biometrics is a scientific journal emphasizing the role of statistics and mathematics in the biological sciences. Its object is to promote and extend the use of mathematical and statistical methods in pure and applied biological sciences by describing developments in these methods and their applications in a form readily assimilable by experimental scientists. JSTOR provides a digital archive of the print version of Biometrics. The electronic version of Biometrics is available at http://www.blackwell-synergy.com/servlet/useragent?func=showIssues&code=biom. Authorized users may be able to access the full text articles at this site.
Biometry --- Biometry. --- Biostatistics. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Statistical methods --- Biomathematics --- Statistics --- Anthropology, Physical --- Statistics as Topic --- 42.11 biomathematics. --- Biométrie --- Biometrie. --- Biometria
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Biometry --- Biometry. --- Biométrie. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Statistical methods --- Biométrie. --- Biomathematics --- Statistics --- Anthropology, Physical --- Statistics as Topic --- Biométrie --- biometrics. --- Health and Wellbeing.
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