This page contains resources relating to statistics for research.
Basic & Clinical Biostatistics, 5e by
A Doody's Core Title for 2020! Basic & Clinical Biostatistics provides medical students, researchers, and practitioners with the knowledge needed to develop sound judgment about data applicable to clinical care. This fifth edition has been updated throughout to deliver a comprehensive, timely introduction to biostatistics and epidemiology as applied to medicine, clinical practice, and research. Particular emphasis is on study design and interpretation of results of research.
An Introduction to Medical Statistics, 4e by
Written in an easy-to-understand style and packed with real life examples, the text clearly explains the statistical principles used in the medical literature.Taking readers through the common statistical methods seen in published research and guidelines, the text focuses on how to interpret and analyse statistics for clinical practice.
Applied Statistical Considerations for Clinical Researchers by
This essential book details intermediate-level statistical methods and frameworks for the clinician and medical researcher with an elementary grasp of health statistics and focuses on selecting the appropriate statistical method for many scenarios. Detailed evaluation of various methodologies familiarizes readers with the available techniques and equips them with the tools to select the best from a range of options.
Diagnostic Meta-Analysis: A Useful Tool for Clinical Decision-Making by
This book is the first exclusively devoted to the systematic synthesis of diagnostic test accuracy studies. It builds upon the major recent developments in reporting standards, search methods, and, in particular, statistical tools specifically devoted to diagnostic studies. In addition, it borrows extensively from the latest advances in systematic reviews and meta-analyses of intervention studies. After a section dedicated to methods for designing reviews, synthesizing evidence and appraising inconsistency in research, the application of these approaches is demonstrated in the context of case studies from various clinical disciplines. Diagnosis is central in medical decision-making, and in many other fields of human endeavor, such as education and psychology. The plurality of sources of evidence on diagnostic test accuracy poses a huge challenge for practitioners and researchers, as do the multiple dimensions of evidence validity, which include sensitivity, specificity, predictive values, and likelihood ratios. This book offers an invaluable resource for anyone aiming to improve decision-making processes in diagnosis, classification or risk prognostication, from epidemiologists to biostatisticians, radiologists, laboratory physicians and graduate students, as any physician interested in refining his methodological skills in clinical diagnosis.
Medical Statistics: For Beginners by
This book deals with statistics in medicine in a simple way. The text is supported by abundant examples from medical data. This book aims to explain and simplify the process of data presentation. Further aspects addressed include how to design and conduct clinical trials, and how to write journal articles.
Modern Bayesian Statistics in Clinical Research by
The current textbook has been written as a help to medical / health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them. SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.). Modern Bayesian statistics is based on biological likelihoods, and may better fit clinical data than traditional tests based normal distributions do. This is the first edition to systematically imply modern Bayesian statistics in traditional clinical data analysis. This edition also demonstrates that Markov Chain Monte Carlo procedures laid out as Bayesian tests provide more robust correlation coefficients than traditional tests do. It also shows that traditional path statistics are both textually and conceptionally like Bayes theorems, and that structural equations models computed from them are the basis of multistep regressions, as used with causal Bayesian networks.
Modern Meta-Analysis: Review and Update of Methodologies by
Modern meta-analyses do more than combine the effect sizes of a series of similar studies. Meta-analyses are currently increasingly applied for any analysis beyond the primary analysis of studies, and for the analysis of big data. This 26-chapter book was written for nonmathematical professionals of medical and health care, in the first place, but, in addition, for anyone involved in any field involving scientific research. The authors have published over twenty innovative meta-analyses from the turn of the century till now. This edition will review the current state of the art, and will use for that purpose the methodological aspects of the authors' own publications, in addition to other relevant methodological issues from the literature. Are there alternative works in the field? Yes, there are, particularly in the field of psychology. Psychologists have invented meta-analyses in 1970, and have continuously updated methodologies. Although very interesting, their work, just like the whole discipline of psychology, is rather explorative in nature, and so is their focus to meta-analysis. Then, there is the field of epidemiologists. Many of them are from the school of angry young men, who publish shocking news all the time, and JAMA and other publishers are happy to publish it. The reality is, of course, that things are usually not as bad as they seem. Finally, some textbooks, written by professional statisticians, tend to use software programs with miserable menu programs and requiring lots of syntax to be learnt. This is prohibitive to clinical and other health professionals. The current edition is the first textbook in the field of meta-analysis entirely written by two clinical scientists, and it consists of many data examples and step by step analyses, mostly from the authors' own clinical research.
Practical Biostatistics in Translational Healthcare by
There is an ever-increasing emphasis on evidence-based medicine that is distinguished by systematic crafting of the patient-centered research question, detailed literature searches, and careful appraisal of the resulting evidence. The consensus that ultimately emerges must then be applied in specific clinical settings, and it is to this process that translational effectiveness analysis refers. This portable and easy-to-use handbook is intended as a practical teaching guide on translational effectiveness for students and clinicians. Specifically, it will serve as a primer on patient-centered outcomes research methodology in the health sciences and explain how to acquire and understand the fundamental data that determine which reports are valued as the "best available" evidence. It presents an accessible and readily intelligible set of principles which doctors, dentists, nurses, and insurance carriers will be able to use in the process of health care-related decision-making.
Recent Advances in Medical Statistics by
This book illustrates the use of complex statistical models in medical and health sciences. The use of these models by researchers contributes to the understanding of more complex processes and thus advances knowledge. The various chapters describe how an ordinal logistic model is applied to disabilities, a model in structural equations to an emergency code, multi-state models for the analysis of time-to-event data and Copula models to agitation-sedation scores, spatial models to epidemiology, and statistical methods to clinical trials and to N-of-1 trial design.
R for Basic Biostatistics in Medical Research by
The scientific community at the global level is fast becoming aware of the rising use of open-source tools such as R and Python for data analysis. Unfortunately, in spite of the awareness, the conversion of the intrigue to the practical knowledge in utilization of the open-source tools for routine day-to-day data analysis is seriously lacking both among physicians and medical scientists. This book enables physician-scientists to understand the complexity of explaining a programming/ data-analytic language to a healthcare professional and medical scientist. It simplifies and explains how R can be used in medical projects and routine office works. It also talks about the methodologies to convert the knowledge to practice. The book starts with the introduction to the structure of R programming language in the initial chapters, followed with explanations of utilizing R in the basics of data analysis like data importing and exporting, operations on a data frame, parametric and non-parametric tests, regression, sample size calculation, survival analysis, receiver operator characteristic analysis (ROC) and techniques of randomization. Each chapter provides a brief introduction to the involved statistics, for example, dataset, working codes, and a section explaining the codes. In addition to it, a chapter has been dedicated to describing the ways to generate plots using R. This book primarily targets health care professionals and medical/life-science researchers in general.
Statistics and Research Methods for Acute Care and General Surgeons by
The main aim of this book is to offer an easy tool to read a scientific article with greater awareness, to understand and evaluate it more thoroughly, and to better plan research. Today, in the era of evidence-based medicine, both research and daily patient-focused clinical practice are no longer possible without a thorough knowledge of the literature and its continuous updates. Written by surgeons for surgeons, this practical book makes the basic concept of statistics and research methodology easy to understand and apply for young surgeons and researchers, students and residents.
Statistics for Clinicians by
This book provides clinical medicine readers with a detailed explanation of statistical concepts using non-technical terms. This allows clinicians and others without specialist statistical knowledge to understand the medical literature where such concepts are used. Many examples from the medical literature are used to exemplify how these concepts are used in practice. Current books written for clinicians fall into two broad categories. Simple texts that are not designed to cover many important statistical concepts used in the medical literature. Comprehensive texts which cover many statistical principles in detail, including statistical theory, but which are more challenging to read and do not always cover many important statistical techniques used in the medical literature. This book assists in the understanding of these techniques. Statistics for Clinicians covers such topics in a robust non-technical manner accessible to clinicians and is intended for hospital consultants, junior doctors and general practitioners. Undergraduates in biomedical sciences and medicine may also find some sections valuable.
Statistics for Clinicians: How Much Should a Doctor Know? by
How much statistics does a clinician, surgeon or nurse need to know? This book provides an essential handbook to help appraise evidence in a scientific paper, to design and interpret the results of research correctly, to guide our students and to review the work of our colleagues. This title is written by a clinician exclusively for fellow clinicians, in their own language and not in statistical or epidemiological terms. When clinicians discuss probability, it is focussed on how it applies to the management of patients in the flesh and how they are managed in a clinical setting. Statistics for Clinicians does not overlook the basis of statistics, but reviews techniques specific to medicine with an emphasis on their application. It ensures that readers have the correct tools to hand, including worked examples, guides and links to online calculators and free software, enabling readers to execute most statistical calculations. This book will therefore be enormously helpful for many working across all fields of medicine at any stage of their career.
Textbook of Medical Statistics: For Medical Students by
This book introduces basic concepts, principle, and methods of medical statistics systematically and practically, especially in the statistical design of the experiment in terms of the specific problems, adequate use of statistical methods based on actual data and the reasonable explanation for statistical results. This textbook combines statistical methods with the common application of SPSS software, which is flexible, convenient, and user-friendly; thus, students can focus on the deep understanding of statistics. The authors emphasize the application and generalization of statistical methods, and combine these methods with the modern statistical theory, such as sequential contingency table and multivariate statistical modelling, etc. This book is a useful textbook for graduate and undergraduate students in medical schools, including MBBS (Bachelor of Medicine and Bachelor of Surgery) student.
Understanding Clinical Data Analysis: Learning Statistical Principles from Published Clinical Research by
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? Thebook will cover the WHY-SOs.
Safety of Anaesthesia: A Review of Anaesthesia-related Mortality Reporting in Australia and New Zealand 2018-2020 by
The 12th triennial Safety of Anaesthesia report has been released and confirms that Australia and New Zealand continue to be incredibly safe countries in which to receive an anaesthetic.
Reflecting the college’s high training standards, and the use of modern airway management tools, data shows this triennium there were no reported Category 1 deaths caused by failures of airway management. Most reported Category 1 deaths related to anaphylaxis, and pulmonary aspiration or its sequalae.
Consistent with previous years, most reported anaesthesia-related deaths related to emergency surgery
(75 per cent). The medical condition of the patient also continued to be a major contributor of reported anaesthesia-related mortality.
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