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Cambridge Dictionary of Statistics in the Medical Sciences

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This dictionary provides over 2,000 brief but useful definitions of statistical terms frequently found in the medical and medical statistics literature. Where appropriate, the author illustrates terms with pictures or numerical examples, and minimizes the use of mathematical formulas. This book will be an essential reference for workers in all branches of medicine, applied statistics and biostatistics.

Best The Progress of Experiment: Science and Therapeutic Reform in the United States, 1900-1990 (Cambridge Studies in the History of Medicine)

The Progress of Experiment: Science and Therapeutic Reform in the United States, 1900-1990 (Cambridge Studies in the History of Medicine) by Cambridge University Press




This book explores the origins of our contemporary system of drug regulation and the modern clinical trial. Marks illustrates the symbiotic relationship between the history of modern drug regulation and the history of therapeutic reform. Accompanying this history of public policy is a detailed account of changing experimental ideals and practices. Marks traces the history of therapeutic experimentation, from the "collective investigations" of the past century to the controlled clinical trial that emerged after 1950 as the paradigm of scientific experimentation. The result is the first general history of clinical research in the United States, a book that examines therapeutic experiments in a wide range of diseases, from syphilis and pneumonia to heart disease and diabetes.Read more The Progress of Experiment: Science and Therapeutic Reform in the United States, 1900-1990 (Cambridge Studies in the History of Medicine)

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Health professionals are increasingly drawn towards research in the course of their training, often with little guidance on the skills and techniques required. This practical text provides essential advice to guide the beginner through the processes involved in selecting, organizing, funding, undertaking, evaluating and publishing a biomedical research project, and completing a postgraduate qualification. Cartoon illustrations provide a wry commentary on the ups and downs of life in research. This edition contains new content on choosing supervisors, career implications, degree structures, ethical issues and time conflicts between research and service needs, and is fully revised and updated to reflect changes in the structure of medical careers and degree courses, and the use of computational and information technology in research presentation. An invaluable guide suitable for all stages of a medical research career, from the first stages of research, through to professional supervisors, career counselors and postgraduate tutors.

Best Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine)

Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) by Cambridge University Press




For biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10.
Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.Read more Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine)

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