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＜電子ブック＞
Applying Quantitative Bias Analysis to Epidemiologic Data / by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash
(Statistics for Biology and Health. ISSN:21975671)

版	2nd ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XVI, 467 p. 76 illus., 39 illus. in color : online resource
著者標目	*Fox, Matthew P author MacLehose, Richard F author Lash, Timothy L author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Epidemiology LCSH:Bioinformatics LCSH:Public health LCSH:Medical informatics LCSH:Biotechnology FREE:Biostatistics FREE:Epidemiology FREE:Bioinformatics FREE:Public Health FREE:Health Informatics FREE:Biotechnology
一般注記	1. Introduction and Objectives -- 2. A Guide to Implementing Quantitative Bias Analysis -- 3. Data Sources for Bias Analysis -- 4. Selection Bias -- 5. Uncontrolled Confounders -- 6. Misclassification -- 7. Measurement Error for Continuous Variables -- 8. Multiple Bias Modeling -- 8. Bias Analysis by Simulation for Summary Level Data -- 9. Bias Analysis by Simulation for Record Level Data -- 10. Combining Systematic and Random Error -- 11. Bias Analysis by Missing Data Methods -- 12. Bias Analysis by Empirical Methods -- 13. Bias Analysis by Bayesian Methods -- 14. Multiple Bias Modeling -- 15. Good Practices for Quantitative Bias Analysis -- 15. Presentation and Inference -- References -- Index This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices HTTP:URL=https://doi.org/10.1007/978-3-030-82673-4

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