Modern Biostatistical Principles And Concepts
Download Modern Biostatistical Principles And Concepts PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Modern Biostatistical Principles And Concepts book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Modern Biostatistical Principles and Concepts
Author: Laurens Holmes, Jr
language: en
Publisher: Laurens Holmes, Jr
Release Date: 2025-03-19
Modern Biostatistical Principles and Concepts - Clinical Medicine and Public/Population Health Assessment Clinical medicine or surgery continues to make advances through evidence that is judged to be objectively drawn from the care of individual patients. The natural observation of individuals remains the basis for our researchable questions’ formulation and the subsequent hypothesis testing. Evidence-based medicine or surgery depends on how critical we are in evaluating evidence in order to inform our practice. These evaluations no matter how objective are never absolute but probabilistic, as we will never know with absolute certainty how to treat future patients who were not a part of our study. Despite the obstacles facing us today in an attempt to provide an objective evaluation of our patients, since all our decisions are based on a judgment of some evidence, we have progressed from expert opinion to the body of evidence from randomized controlled clinical trials, as well as cohort investigations, prospective and retrospective. The conduct of clinical trials though termed the “gold standard”, which yields more reliable and valid evidence from the data relative to non-experimental or observational designs, depends on how well it is designed and conducted prior to outcomes data collection, analysis, results, interpretation, and dissemination. The designs and the techniques used to draw statistical inferences are often beyond the average clinician’s understanding. A text that brings hypothesis formulation, analysis, and how to interpret the results of the findings is long overdue and highly anticipated. Statistical modeling which is fundamentally a journey from sample to the application of findings is essential to evidence discovery. This text, Modern Biostatistics for Clinical, Biomedical and Population-Based Researchers has filled this gap, not only in the way complex modeling is explained but the simplification of statistical techniques in a way that had never been explained before. This text has been prepared intentionally at the rudimentary level to benefit clinicians without sophisticated mathematical backgrounds or previous advanced knowledge of biostatics as applied statistics in health and medicine. Also, biomedical researchers who may want to conduct clinical research, as well as consumers of research products may benefit from the sampling techniques, their relevance to scientific evidence discovery as well a simplified approach to statistical modeling of clinical and biomedical research data. It is with this expectation and enthusiasm that we recommend this text to clinicians in all fields of clinical and biomedical research. One’s experience with biomedical research and how the findings in this arm are translated to the clinical environment signals the need for the application of biological, and clinical relevance of findings prior to statistical inference. The examples provided by the author to simplify research methods are familiar to orthopedic surgeons as well as clinicians in other specialties of medicine and surgery. Whereas statistical inference is essential in our application of the research findings to clinical decision-making regarding the care of our patients, statistical inference without clinical relevance or importance can be very misleading, and meaningless. The authors have attempted to deemphasize the p-value in the interpretation of clinical and biomedical research findings, by stressing the importance of confidence intervals, which allow for the quantification of evidence. For example, a large study due to a large sample size that minimizes variability may show a statistically significant difference while in reality, the difference is too insignificant to warrant any clinical importance. In contrast, a small study as frequently seen in most clinical trials or surgical research may have a large effect size of clinical relevance but not statistically significant at (p > 0.05). Thus, without considering the magnitude of the effect size with the confidence interval, we tend to regard these studies as negative findings, which is erroneous, since the absence of evidence, simply on the basis of an arbitrary significance level of 5% does not necessarily mean evidence of absence.1 In effect, clinical research results, cannot be adequately interpreted without first considering the biological and clinical significance of the data, before the statistical stability of the findings (p-value and 95% Confidence Interval), since the p-value as observed by the authors merely reflects the size of the study and not the measure of evidence. In recommending this text, it is one’s inclination that this book will benefit clinicians, research fellows, clinical fellows, postdoctoral students in biomedical and clinical settings, nurses, clinical research coordinators, physical therapists, and all those involved in clinical research design, conduct, and analysis of research data for statistical and clinical relevance. Convincingly, knowledge gained from this text will lead to our improvement of patient care through well-conceptualized research. Therefore, with the knowledge that no book is complete, no matter its content or volume, especially a book of this nature, which is prepared to guide clinicians on sampling, statistical modeling of data, and interpretation of findings, this book will benefit clinicians who are interested in applying appropriate statistical technique to scientific evidence discovery. Finally, we are optimistic that this book will bridge the gap in knowledge and practice of clinical and biomedical research, especially for clinicians in busy practice who are passionate about making a difference in their patient's care through scientific research initiatives.
Biostatistics
The ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences. Now in its 11th edition, Biostatistics: A Foundation for Analysis in the Health Sciences continues to offer in-depth guidance toward biostatistical concepts, techniques, and practical applications in the modern healthcare setting. Comprehensive in scope yet detailed in coverage, this text helps students understand—and appropriately use—probability distributions, sampling distributions, estimation, hypothesis testing, variance analysis, regression, correlation analysis, and other statistical tools fundamental to the science and practice of medicine. Clearly-defined pedagogical tools help students stay up-to-date on new material, and an emphasis on statistical software allows faster, more accurate calculation while putting the focus on the underlying concepts rather than the math. Students develop highly relevant skills in inferential and differential statistical techniques, equipping them with the ability to organize, summarize, and interpret large bodies of data. Suitable for both graduate and advanced undergraduate coursework, this text retains the rigor required for use as a professional reference.
Fundamentals of Modern Statistical Methods
Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size.