| Description |
1 online resource (xii, 314 pages) : illustrations, maps |
| Contents |
Confounding and Causal Path Diagrams / Graham R. Law, Rosie Green and George T.H. Ellison -- Statistical Modelling of Partially Observed Data Using Multiple Imputation: Principles and Practice / James R. Carpenter, Harvey Goldstein and Michael G. Kenward -- Measurement Errors in Epidemiology / Darren C. Greenwood -- Selection Bias in Epidemiologic Studies / Graham R. Law, Paul D. Baxter and Mark S. Gilthorpe -- Multilevel Modelling / Andrew Blance -- Modelling Data That Exhibit an Excess Number of Zeros: Zero-Inflated Models and Generic Mixture Models / Mark S. Gilthorpe, Morten Frydenberg, Yaping Cheng and Vibeke Baelum -- Multilevel Latent Class Modelling / Wendy Harrison, Robert M. West, Amy Downing and Mark S. Gilthorpe -- Bayesian Bivariate Disease Mapping / Richard G. Feltbower and Samuel O.M. Manda -- A Multivariate Random Frailty Effects Model for Multiple Spatially Dependent Survival Data / Samuel O.M. Manda, Richard G. Feltbower and Mark S. Gilthorpe -- Meta-analysis of Observational Studies / Darren C. Greenwood -- Directed Acyclic Graphs and Structural Equation Modelling / Yu-Kang Tu -- Latent Growth Curve Models / Yu-Kang Tu and Francesco D'Auito -- Growth Mixture Modelling for Life Course Epidemiology / Darren L. Dahly -- G-estimation for Accelerated Failure Time Models / Kate Tilling, Jonathan A.C. Sterne and Vanessa Didelez -- Generalised Additive Models / Robert M. West -- Regression and Classification Trees / Robert M. West -- Statistical Interactions and Gene-Environment Joint Effects / Mark S. Gilthorpe and David G. Clayton |
| Summary |
Routine applications of advanced statistical methods on real data have become possible in the last ten years because desktop computers have become much more powerful and cheaper. However, proper understanding of the challenging statistical theory behind those methods remains essential for correct application and interpretation, and rarely seen in the medical literature. Modern Methods for Epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers. Many of these methods have become indispensible tools for researchers working in epidemiology and medicine but are rarely discussed in details by standard textbooks of biostatistics or epidemiology. Contributors of this book are experienced researchers and experts in their respective fields. This textbook provides a solid starting point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational epidemiology. Epidemiological and biomedical researchers who wish to overcome the mathematical barrier of applying those methods to their research will find this book an accessible and helpful reference for self-learning and research. This book is also a good source for teaching postgraduate students in medical statistics or epidemiology |
| Analysis |
Biomedicine |
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Biostatistics |
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Cancer Research |
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Medicine |
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Oncology |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Print version record |
| Subject |
Epidemiology -- Research -- Methodology
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Epidemiology -- Statistical methods
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Public health.
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Medical care.
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Epidemiologic Methods
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Investigative Techniques
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Public Health
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Environment and Public Health
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Analytical, Diagnostic and Therapeutic Techniques and Equipment
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Delivery of Health Care
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Patient Care
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public health.
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MEDICAL -- Epidemiology.
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MEDICAL -- Health Risk Assessment.
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Public health
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Medical care
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Epidemiology -- Research -- Methodology
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Epidemiology -- Statistical methods
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| Form |
Electronic book
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| Author |
Tu, Yu-Kang.
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Greenwood, Darren C.
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| ISBN |
9789400730243 |
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9400730241 |
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9400794061 |
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9789400794061 |
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