A Survey of Statistical Usage in the Recent Medical Literature and a Statistical Reasoning Test for Resident Physicians PDF Download
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Author: John Logan Zintsmaster
Publisher:
ISBN:
Category : Medical statistics
Languages : en
Pages : 120
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Book Description
Author: John Logan Zintsmaster
Publisher:
ISBN:
Category : Medical statistics
Languages : en
Pages : 120
Get Book Here
Book Description
Author: Lemuel A. Moye
Publisher: Springer Science & Business Media
ISBN: 1475732929
Category : Medical
Languages : en
Pages : 301
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Book Description
Employing a conversational format and consciously de-emphasizing computational devices, this text focuses instead on the features of experimental design that either clarify or blur p value interpretation, so as to make statistical reasoning accessible to the uninitiated. Through careful, deliberate thought this book provides the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinical research. It recognizes the inevitable tension between the mathematics of hypothesis testing and the ethical requirements in medical research and concentrates on resolving these issues in p value interpretation.
Author: John C. Bailar III
Publisher: CRC Press
ISBN: 0429529600
Category : Mathematics
Languages : en
Pages : 490
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Book Description
This work explains the purpose of statistical methods in medical studies and analyzes the statistical techniques used by clinical investigators, with special emphasis on studies published in "The New England Journal of Medicine". It clarifies fundamental concepts of statistical design and analysis, and facilitates the understanding of research results.
Author: Alfred DeMaris
Publisher: Springer Science & Business Media
ISBN: 1461477921
Category : Medical
Languages : en
Pages : 231
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Book Description
Converting Data into Evidence: A Statistics Primer for the Medical Practitioner provides a thorough introduction to the key statistical techniques that medical practitioners encounter throughout their professional careers. These techniques play an important part in evidence-based medicine or EBM. Adherence to EBM requires medical practitioners to keep abreast of the results of medical research as reported in their general and specialty journals. At the heart of this research is the science of statistics. It is through statistical techniques that researchers are able to discern the patterns in the data that tell a clinical story worth reporting. The authors begin by discussing samples and populations, issues involved in causality and causal inference, and ways of describing data. They then proceed through the major inferential techniques of hypothesis testing and estimation, providing examples of univariate and bivariate tests. The coverage then moves to statistical modeling, including linear and logistic regression and survival analysis. In a final chapter, a user-friendly introduction to some newer, cutting-edge, regression techniques will be included, such as fixed-effects regression and growth-curve modeling. A unique feature of the work is the extensive presentation of statistical applications from recent medical literature. Over 30 different articles are explicated herein, taken from such journals. With the aid of this primer, the medical researcher will also find it easier to communicate with the statisticians on his or her research team. The book includes a glossary of statistical terms for easy access. This is an important reference work for the shelves of physicians, nurses, nurse practitioners, physician’s assistants, medical students, and residents.
Author: Ton J. Cleophas
Publisher: Springer Science & Business Media
ISBN: 9400728638
Category : Medical
Languages : en
Pages : 739
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Book Description
Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to closely observe the latest developments in the field of clinical data analysis, and they have been able to assess their performance. In this 5th edition the 47 chapters of the previous edition have been maintained and upgraded according to the current state of the art, and 20 novel chapters have been added after strict selection of the most valuable and promising novel methods. The novel methods are explained using practical examples and step-by-step analyses readily accessible for non-mathematicians. All of the novel chapters have been internationally published by the authors in peer-reviewed journal, including the American Journal of Therapeutics, the European Journal of Clinical Investigation, The International journal of Clinical Pharmacology and therapeutics, and other journals, and permission is granted by all of them to use this material in the current book. We should add that the authors are well-qualified in their fields of knowledge. Professor Zwinderman is president-elect of the International Society of Biostatistics, and Professor Cleophas is past-president of the American College of Angiology. From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors, although from a different discipline, one clinician and one statistician, have been working and publishing together for over 10 years, and their research of statistical methodology can be characterized as a continued effort to demonstrate that statistics is not mathematics but rather a discipline at the interface of biology and mathematics. They firmly believe that any reader can benefit from this clinical approach to statistical data analysis.
Author: Mitchell G. Maltenfort
Publisher: CRC Press
ISBN: 1351608371
Category : Medical
Languages : en
Pages : 115
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Book Description
Trying to read up on statistics can be like trying to decide where you want to start eating the elephant and what’s the most digestible way to get it down. This book is written to give bite-size nuggets of insight based on our experiences grappling with datasets large and small. It is intended to bridge the gap between the formal equations and the practicalities of generating a research manuscript. We won’t pretend reading it will answer all your questions but it will help explain what questions need to be asked for your study and how you can address them with both accuracy and clarity. The size, detail and (ostensible) organization of this book allow for easy reading and can give a leg (or at least a half-step) up for those seeking more detailed study later. Features include: Excel sheets to allow exploration of topics raised Emphasis on intuitive explanations over formulas. Consideration of issues specific to clinical and surgical studies Our audience is someone who may or may not have enjoyed formal statistics education (that is, you may have had it and not enjoyed it!) who may like seeing a more dressed-down presentation of the topics. Actual statisticians may pick this up at risk of a chuckle (with us or at us) and may find some useful ways to present topics to non-statisticians.
Author: William H. Holmes
Publisher: Springer
ISBN: 3319125508
Category : Medical
Languages : en
Pages : 487
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Book Description
This textbook on statistics is written for students in medicine, epidemiology, and public health. It builds on the important role evidence-based medicine now plays in the clinical practice of physicians, physician assistants and allied health practitioners. By bringing research design and statistics to the fore, this book can integrate these skills into the curricula of professional programs. Students, particularly practitioners-in-training, will learn statistical skills that are required of today’s clinicians. Practice problems at the end of each chapter and downloadable data sets provided by the authors ensure readers get practical experience that they can then apply to their own work.
Author: Filomena Pereira-Maxwell
Publisher: CRC Press
ISBN: 1444167359
Category : Medical
Languages : en
Pages : 417
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Book Description
Clear and user-friendly A-Z format, in handy a pocket size, allows speedy access to information in all settings Fully updated and expanded to cover over 500 statistical terms for comprehensive coverage Enhanced explanations of statistical concepts and methods, including more illustrative content, for greater accessibility Frequent use of examples from the medical literature, with reference to landmark studies, ensures clinical relevance Those new to medical statistics and the more experienced reader will find something of interest here
Author: Thomas Allen Lang
Publisher: ACP Press
ISBN: 1930513690
Category : Medical
Languages : en
Pages : 512
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Book Description
This volume presents a comprehensive and comprehensible set of guidelines for reporting the statistical analyses and research designs and activities commonly used in biomedical research.
Author: James Penston
Publisher:
ISBN: 9781907313332
Category : Mathematics
Languages : en
Pages : 318
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Book Description
About Stats.con - How we've been fooled by statistics-based research in medicine: Statistics-based research is the method by which the causes of disease and the effectiveness of new treatments are investigated. Epidemiological studies and large-scale randomised controlled trials dominate medical research. Judged by the number of papers published each year, this type of research would appear to be a success. Yet it s a triumph of appearance over substance. We ve been cajoled into believing that great advances in medicine have occurred when, in fact, this isn t the case. Large RCTs are placed at the summit of the hierarchy of evidence and are claimed to be the most reliable means of establishing causal relationships in medical research. They are highly complex structures designed to identify small differences in outcome between the active treatment group and controls. But how do we know that the observed difference is caused by the drug? Proponents of RCTs assert that the method excludes alternative explanations namely, the unequal distribution of other causal factors, bias in the assessment of the outcome and chance. In other words, they believe that these studies have internal validity. The primary thesis of stats.con is that the grounds for causal inference in statistics-based research are lacking. Firstly, the components of the RCT including randomisation, allocation concealment, double-blind administration of treatment, the handling of withdrawals and drop-outs, and the statistical tests don t guarantee that the conditions for internal validity have been satisfied. Secondly, the frequentist approach to statistics, which continues to be used in almost all medical research studies despite being subjected to serious criticisms in recent years, is unsound. Thirdly, and most importantly, the inference from a small difference in outcome to the presence of a causal relationship is highly questionable. Given these arguments, it s of some importance to note that neither the results of individual RCTs nor the statistical method in general can be tested independently. This is an inevitable consequence of the subject matter of this type of research which involves heterogeneous samples with unknown mixtures of constituents. The inability to test the results of statistics-based research is of particular concern as fraud is more common than hitherto supposed in medical research. But even if we were to accept the validity of causal inference in this situation and to dismiss concerns about independent testing, we would still face the unpalatable truth that the product of statistics-based research is of little value. The reliability of any generalisation from the results of an individual study to the wider population of patients that is, the external validity is always open to question. We can never know whether the results of a RCT apply to either a particular patient or to a specified group. This is an enormous disadvantage in medicine. But that s not all. The size of the treatment effect in large-scale studies is very small. Indeed, it s so small that the true size of the effect is deliberately hidden by researchers and others with a vested interest in the outcome of the studies. When we look closely, the product of these studies is of dubious worth and doubtful meaning. The reasons for the widespread acceptance of statistics-based research are to be found in the events of the past fifty years or more. History shows how the advocates have used every means at their disposal to spread a flawed methodology and how their views have infiltrated the thinking of generations of researchers, practicing physicians and others involved in the care of patients. But this doesn t apply only to medical research. Many other academic disciplines use similar methods. If, as is argued in stats.con, the case against statistics-based research is made, then the implications extend far beyond the field of medicine.
