Stats.con – How we’ve been fooled by statistics-based research in medicine

Author by : James Penston
Languange Used : en
Release Date : 2010
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ISBN :

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....

Author by : Wisconsin. Attorney General's Office
Languange Used : en
Release Date : 1916
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Author by :
Languange Used : en
Release Date : 1997
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Author by : Peter M. B. Cahusac
Languange Used : en
Release Date : 2020-09-03
Publisher by : John Wiley & Sons

ISBN :

Description : Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice provides readers with a comprehensive and thorough guide to the evidential approach in statistics. The approach uses likelihood ratios, rather than the probabilities used by other statistical inference approaches. The evidential approach is conceptually easier to grasp, and the calculations more straightforward to perform. This book explains how to express data in terms of the strength of statistical evidence for competing hypotheses. The evidential approach is currently underused, despite its mathematical precision and statistical validity. Evidence-Based Statistics is an accessible and practical text filled with examples, illustrations and exercises. Additionally, the companion website complements and expands on the information contained in the book. While the evidential approach is unlikely to replace probability-based methods of statistical inference, it provides a useful addition to any statistician’s “bag of tricks.” In this book: It explains how to calculate statistical evidence for commonly used analyses, in a step-by-step fashion Analyses include: t tests, ANOVA (one-way, factorial, between- and within-participants, mixed), categorical analyses (binomial, Poisson, McNemar, rate ratio, odds ratio, data that’s ‘too good to be true’, multi-way tables), correlation, regression and nonparametric analyses (one sample, related samples, independent samples, multiple independent samples, permutation and bootstraps) Equations are given for all analyses, and R statistical code provided for many of the analyses Sample size calculations for evidential probabilities of misleading and weak evidence are explained Useful techniques, like Matthews’s critical prior interval, Goodman’s Bayes factor, and Armitage’s stopping rule are described Recommended for undergraduate and graduate students in any field that relies heavily on statistical analysis, as well as active researchers and professionals in those fields, Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice belongs on the bookshelf of anyone who wants to amplify and empower their approach to statistical analysis....

Author by : Charles Kufs
Languange Used : en
Release Date : 2011
Publisher by : Wheatmark, Inc.

ISBN :

Description : When you took statistics in school, your instructor gave you specially prepared datasets, told you what analyses to perform, and checked your work to see if it was correct. Once you left the class, though, you were on your own. Did you know how to create and prepare a dataset for analysis? Did you know how to select and generate appropriate graphics and statistics? Did you wonder why you were forced to take the class and when you would ever use what you learned? That's where Stats with Cats can help you out. The book will show you: How to decide what you should put in your dataset and how to arrange the data. How to decide what graphs and statistics to produce for your data. How you can create a statistical model to answer your data analysis questions. The book also provides enough feline support to minimize any stress you may experience. Charles Kufs has been crunching numbers for over thirty years, first as a hydrogeologist, and since the 1990s as a statistician. He is certified as a Six Sigma Green Belt by the American Society for Quality. He currently works as a statistician for the federal government and he is here to help you....

Author by : Patricia de Winter
Languange Used : en
Release Date : 2014-09-02
Publisher by : John Wiley & Sons

ISBN :

Description : To form a strong grounding in human-related sciences it is essential for students to grasp the fundamental concepts of statistical analysis, rather than simply learning to use statistical software. Although the software is useful, it does not arm a student with the skills necessary to formulate the experimental design and analysis of a research project in later years of study or indeed, if working in research. This textbook deftly covers a topic that many students find difficult. With an engaging and accessible style it provides the necessary background and tools for students to use statistics confidently and creatively in their studies and future career. Key features: Up-to-date methodology, techniques and current examples relevant to the analysis of large data sets, putting statistics in context Strong emphasis on experimental design Clear illustrations throughout that support and clarify the text A companion website with explanations on how to apply learning to related software packages This is an introductory book written for undergraduate biomedical and social science students with a focus on human health, interactions, and disease. It is also useful for graduate students in these areas, and for practitioners requiring a modern refresher....

Author by : Gilbert Burnet
Languange Used : en
Release Date : 1837
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Author by : Massachusetts. Bureau of Statistics
Languange Used : en
Release Date : 1918
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Languange Used : en
Release Date : 1896
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Author by : Canada. Department of Justice
Languange Used : en
Release Date : 1896
Publisher by : Government Print. Bureau

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