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| By: Chap T. Le ISBN: 0471418161 Publisher: Wiley-Interscience Release Date: 31 March, 2003 Bioscience book rank: 750484 | It is mind-boggling how one can have so many mistakes in a textbook like this. This book was required for our Biostatistics II class, and I am really displeased by all of the mistakes. What is the worst offense is that some of the answers for test questions are wrong. For example, the one way ANOVA problem 7.21 has the wrong F-statistic: the wrong answer. Others in the class have complained about this text as well. This is simply amazing to me, and leaves me not trusting much of the text in this book. <br /> <br />His other book "Health and Numbers" is just as terrible; the professor that used that book for class made a list of the mistakes they found, and us students in that class found many more along the way. I ran into another statistics book that takes all of the procedures step-by-step and really made it simple for me. This book, and his other are not recommended. <br /> | |
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| By: Miroslav Kaps, William R. Lamberson ISBN: 0851998208 Publisher: CABI Release Date: 16 September, 2004 Bioscience book rank: 1015223 | Book was just what I wanted in great condition and arrived fast. thank you. | |
| By: Geoffrey R., Ph.D. Norman, David L. Streiner ISBN: 1550094009 Publisher: BC Decker Inc Release Date: 01 February, 2008 Bioscience book rank: 749302 | ||
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| By: Sylvia Wassertheil-Smoller ISBN: 0387402926 Publisher: Springer Release Date: 11 February, 2004 Bioscience book rank: 412687 | Although billed as a 'primer for health & biomedical professionals', this book is actually too basic for that. Most in the field are not undergrads, and so need the more advanced statistical measuring methods explained more thoroughly (or, in some cases, at all). There is nothing about infective disease, so those measures are also lacking. <br /> <br />This book feels very disorganized, with little help from the index and contents. There are much better books of this type on the market - for example, Primer of Biostatistics by Stanton A. Glantz or even Epidemiology by Leon Gordis. I'd keep looking. | |
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| By: Shein-Chung Chow, Jun Shao ISBN: 082470763X Publisher: CRC Release Date: 20 February, 2002 Bioscience book rank: 1223985 | This is a very new and unique book that covers the gamut of statistical issues through all phases of drug development. Shao is a distinguished professor from Wisconsin and Chow teaches at Duke University and formerly at Temple but is known for his long career in the pharmaceutical industry.
<br />The book is good for biostatisticians and regulatory affairs specialists as a reference source. All the key statistical issues are addressed and the reader is given the perspective of the ICH and FDA guidance documents. The underlying statistical methodology that justifies the recommendations in the guidances is presented. This is a state-of-the-art book. Shao and Pigeot produced some of the recent research in individual bioequivalence that established a bootstrap procedure as an appropriate way to construct confidence intervals for the problem. Their method is recommended in an FDA guidance document.
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<br />But more than just this one example, all the key issues that have been the subject of FDA workshops over the past several years are addressed in this book. These topics include calibration, assay and assay validation, dissolution testing, stability analysis, shelf life estimation, bioequivalence, randomization and blinding, what constitutes substantive evidence in clinical development, therapeutic equivalence and noninferiority, Bayesian approaches in clinical trials, problems involving missing and incomplete data, longitudinal methods, meta-analysis, quality of life studies and instrument validation, and medical imaging.
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<br />Other prevalent issues in clinical trials include group sequential methods, hierarchical Bayesian models and multiple testing. These issues are not covered as much in this text as the others we have mentioned. But there is some discussion of multiplicity in the context of quality of life studies. An example of sequential testing is used to illustrate model selection in Chapter 2. The important issues of design and sample size requirements are presented throughout the book.
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<br />While not all topics are covered in sufficient depth, the book is remarkable in the breadth of material covered in just 350 pages of text. The authors also provide a very authoritative list of references and regulatory guidances and other documents.
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<br /> This is a very new and unique book that covers the gamut of statistical issues through all phases of drug development. Shao is a distinguished professor from Wisconsin and Chow teaches at Temple but is known for his long career in the pharmaceutical industry. <p>The book is good for biostatisticians and regulatory affairs specialists as a reference source. All the key statistical issues are addressed and the reader is given the perspective of the ICH and FDA guidance documents. The underlying statistical methodology that justifies the recommendations in the guidances is presented. This is a state-of-the-art book. Shao and Pigeot produced some of the recent research in individual bioequivalence that established a bootstrap procedure as an appropriate way to construct confidence intervals for the problem. Their method is recommended in an FDA guidance document.<p>But more than just this one example, all the key issues that have been the subject of FDA workshops over the past several years are addressed in this book. These topics include calibration, assay and assay validation, dissolution testing, stability analysis, shelf life estimation, bioequivalence, randomization and blinding, what constitutes substantive evidence in clinical development, therapeutic equivalence and noninferiority, Bayesian approaches in clinical trials, problems involving missing and incomplete data, longitudinal methods, meta-analysis, quality of life studies and instrument validation, and medical imaging.<p>Other prevalent issues in clinical trials include group sequential methods, hierarchical Bayesian models and multiple testing. These issues are not covered as much in this text as the others we have mentioned. But there is some discussion of multiplicity in the context of quality of life studies. An example of sequential testing is used to illustrate model selection in Chapter 2. The important issues of design and sample size requirements are presented throughout the book. <p>While not all topics are covered in sufficient depth, the book is remarkable in the breadth of material covered in just 350 pages of text. The authors also provide a very authoritative list of references and regulatory guidances and other documents. | |
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| By: James E. De Muth ISBN: 0824719670 Publisher: CRC Release Date: 18 June, 1999 Bioscience book rank: 924966 | As the cover of the text would indicate, James DeMuth's "Basic Statistics and Pharmaceutical Statistical Applications" sends the reader on a roller coaster ride through the intriguing world of statitics. In this edition, Dr. Demuth astutely details the methodology necessary to summarize data and make informed decisions about observed outcomes. More importantly though, he provides concrete examples which enable the reader to effectively deploy the information in their day-to-day lives. This edition, undoubtedly the first of many more to come, should prove to be an invaluable resource for students, practicing pharmacists and industrial scientists alike. | |
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| By: Abhaya Indrayan, S.B. Sarmukaddam ISBN: 0824704266 Publisher: CRC Release Date: 15 January, 2001 Bioscience book rank: 1223729 | I have long been looking for a book that makes biostatistics simple and readable to medical professionals. This book on Medical Biostatistics by Indrayan and Sarmukaddam (Marcel Dekker) is a very sincere attempt in making the subject of biostatistics look like a medical discipline. This is one of the objectives stated in the book, and seems to have been very adequately achieved. The language used is friendly to the medical fraternity. A large number of statistical methods have been discussed that are commonly used in medicine and health. Mathematics is minimal and explantions appeal to the common sense. Statistical fallacies are also discussed. Strongly recommended for all professionals and students of medical related disciplines who collect, disseminate or use data in any form. | |
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| By: Lemuel A. Moyé, Asha Seth Kapadia ISBN: 0824704479 Publisher: CRC Release Date: 15 December, 2000 Bioscience book rank: 1395779 | ||
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| By: Mikel Aickin ISBN: 0824707486 Publisher: Chapman & Hall/CRC Release Date: 15 January, 2002 Bioscience book rank: 1351211 | The pioneers of causal modeling are Mackie, Rothman, Shafer, Suppes, Rubin and Pearl. Although much work and debate on the subject came about through the discipline of philosophy, Aickin contends that the philosophers are not about to resolve the issues. It is the scientists particularly the epidemiologists that have the strongest interest.
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<br />Although statisticians are primarily concerned with designing experiments to reach scientific conclusions, few statisticians have ventured into the area of causality. Don Rubin is one notable exception. Aickin is a biostatistician who is trying to make modest progress by elaborating on the need to design studies to deal with causality.
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<br />There have been many naysayers among the philosophers starting with David Hume. Many statisticians trained to warn that correlation does not imply causality assume that causality is not a provable hypothesis.
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<br />Aickin accepts the fact that causality does not have a clean definition and that there are problems with many of the existing constructs. But rather than shoot down exisiting theories he proposes to improve on them. Calculus provides a clear analogy for him. There were philosophical issues with the notion of the limit on which the Calculus is based but this did not hold Newton and Leibniz back from developing the mathematical theory that had many valuable applications. The same could be true with causality.
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<br />Aickin clearly points out the issues with causality in the first chapter. It is not simply a matter of event A causing event B since several events could cause B and could work in concert or in opposition. Aickin builds on the ideas of Mackie and Rothman using in particular concepts of minimally sufficient causality.
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<br />He introduces basic probability and unitary algebra and relates probability models to causal pathways. Medical applications include relationships found in Down's syndrome, obesity and gestational diabetes. In the epilogue he illustrates an instructive causal diagram for mountain sickness. There is also a chapter discussing the theory of directed acyclic graphs.
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<br />A key message is that we need to change the trend of scientific inquiry in medicine in order to design studies to identify causes and exploit causal theories that the physicians and scientists may have.
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<br />When teaching statistics many of us avoid the issue of causality and simply warn when we teach about correlation that "correlation does not imply causality." Unfortunately I think it leads the student to believe that statistical correlation is the only tool we have for exploring relationships between two variables and that causality can never be established. This is misleading I think and more books on causality are needed.
<br /> The pioneers of causal modeling are Mackie, Rothman, Shafer, Suppes, Rubin and Pearl. Although much work and debate on the subject came about through the discipline of philosophy, Aickin contends that the philosophers are not about to resolve the issues. It is the scientists particularly the epidemiologists that have the strongest interest. Although statisticians are primarily concerned with designing experiments to reach scientific conclusions, few statisticians have ventured into the area of causality. Don Rubin is one notable exception. Aickin is a biostatistician who is trying to make modest progress by elaborating on the need to design studies to deal with causality.<p>There have been many naysayers among the philosophers starting with David Hume. Many statisticians trained to warn that correlation does not imply causality assume that causality is not a provable hypothesis.<p>Aickin accepts the fact that causality does not have a clean definition and that there are problems with many of the existing constructs. But rather than shoot down exisiting theories he proposes to improve on them. Calculus provides a clear analogy for him. There were philosophical issues with the notion of the limit on which the Calculus is based but this did not hold Newton and Leibniz back from developing the mathematical theory that had many valuable applications. The same could be true with causality.<p>Aickin clearly point out the issues with causality in the first chapter. It is not simply a matter of event A causing event B since several events could cause B and could work in concert or in opposition. Aickin builds on the ideas of Mackie and Rothman using in particular concepts of minimally sufficient causality.<p>He introduces basic probability and unitary algebra and relates probability models to causal pathways. Medical applications include relationships found in Down's syndrome, obesity and gestational diabetes. In the epilogue he illustrates an instructive causal diagram for mountain sickness. There is also a chapter discussing the theory of directed acyclic graphs.<p>A key message is that we need to change the trend of scientific inquiry in medicine in order to design studies to identify causes and exploit causal theories that the physicians and scientists may have. | |
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| By: Steve Selvin ISBN: 0195172809 Publisher: Oxford University Press, USA Release Date: 13 May, 2004 Bioscience book rank: 737942 | ||
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