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Titlebook: Analysis of Epidemiologic Data Using R; Robert Hirsch Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive license t

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发表于 2025-3-21 16:50:47 | 显示全部楼层 |阅读模式
期刊全称Analysis of Epidemiologic Data Using R
影响因子2023Robert Hirsch
视频videohttp://file.papertrans.cn/157/156361/156361.mp4
发行地址Includes important analyses for epidemiologic data and has a minimal reliance on mathematics.Presents broad coverage from simple descriptions to complex multivariable analyses.Utilizes the freely avai
学科分类Synthesis Lectures on Mathematics & Statistics
图书封面Titlebook: Analysis of Epidemiologic Data Using R;  Robert Hirsch Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive license t
影响因子.This book addresses the description and analysis of occurrence data frequently encountered in epidemiological studies.With the occurrence of Covid-19, people have been exposed to the analysis and interpretation of epidemiological data. To be informed consumers of this information, people need to understand the nature and analysis of these data.Effort is made to emphasize concepts rather than mathematics.Subjects range from description of the frequencies of disease to the analysis of associations between the occurrence of disease and exposure.Those analyses begin with simple associations and work up to complex relationships that involve the control of extraneous characteristics.Analyses rely on the statistical software R, which is freeware in wide use by professional epidemiologists and other scientists..
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Causal Inference,fect. Sufficient causes, those that can produce the effect are almost always a collection of component causes acting together. Most frequently, there are different collections of component causes that are sufficient to produce the effect. The frequency with which a particular component cause occurs
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,2 × 2 Tables, groups using an odds ratio. An odds ratio is close in value to the risk ratio when the disease is rare. Odds ratios have the advantage of not being influenced by whether exposure or disease was sampled. This makes it ideal for case–control studies. We can test hypotheses about proportion difference
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Stratified Analysis,hen a confounder is a cause of the disease and exposure is associated with the confounder. Confounding can be controlled in the design of a study. Two mechanisms for doing that are matching and randomization. They control for confounding by removing the association between the confounder and exposur
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August-Wilhelm Scheer,Christian Wachter four quadrants. The lower left is where the programs are run. The lower right is where the documentation is available and where graphic output appears. The upper right is where datasets are listed, and the upper left is where you can see the content of datasets. Creation of datasets usually starts
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Grundschulbildung in der digitalen Welttion. The best we can do is make samples representative of the population on the average. We do this by having chance be a determinant of the content of the sample. Then, we must take this role of chance into account whether we are making estimates or drawing inferences. One way to take chance into
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