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Statistical Modeling In Biomedical Research

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Statistical Modeling In Biomedical Research

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Author : Yichuan Zhao
language : en
Publisher: Springer Nature
Release Date : 2020-03-19

Statistical Modeling In Biomedical Research written by Yichuan Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-19 with Medical categories.

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its applications.

Statistical Modeling In Biomedical Research

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Author :
language : en
Publisher:
Release Date : 2020

Statistical Modeling In Biomedical Research written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Biometry categories.

Statistical Modeling For Biomedical Researchers

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Author : William Dudley Dupont
language : en
Publisher:
Release Date : 2014-05-14

Statistical Modeling For Biomedical Researchers written by William Dudley Dupont and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-14 with Medical categories.

New edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.

Statistical Modeling For Biomedical Researchers

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Author : William D. Dupont
language : en
Publisher: Cambridge University Press
Release Date : 2009-02-12

Statistical Modeling For Biomedical Researchers written by William D. Dupont and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-12 with Medical categories.

A second edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.

Epidemiology And Medical Statistics

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Author :
language : en
Publisher: Elsevier
Release Date : 2007-11-21

Epidemiology And Medical Statistics written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-21 with Mathematics categories.

This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the development of new methods and innovative adaptations of standard methods. Since the literature is highly scattered, the Editors have undertaken this humble exercise to document a representative collection of topics of broad interest to diverse users. The volume spans a cross section of standard topics oriented toward users in the current evolving field, as well as special topics in much need which have more recent origins. This volume was prepared especially keeping the applied statisticians in mind, emphasizing applications-oriented methods and techniques, including references to appropriate software when relevant.· Contributors are internationally renowned experts in their respective areas· Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research· Methods for assessing Biomarkers, analysis of competing risks· Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs· Structural equations modelling and longitudinal data analysis

Statistical Modeling For Biomedical Researchers

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Author : William D. Dupont
language : en
Publisher: Cambridge University Press
Release Date : 2002-11-28

This text enables biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research, and uses a statistical software package (Stata® ) to avoid mathematics beyond the high school level. Intended for people who have had an introductory course in biostatistics, the volume emphasizes the assumptions underlying each method, using exploratory techniques to determine the most appropriate method. It presents results in a way that will be readily understood by clinical colleagues. Numerous real examples from medical literature and graphical methods are used to illustrate these techniques.

Sourcebook Of Models For Biomedical Research

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Author : P. Michael Conn
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-07

Sourcebook Of Models For Biomedical Research written by P. Michael Conn and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-07 with Medical categories.

The collection of systems represented in this volume is a unique effort to reflect the diversity and utility of models used in biomedicine. That utility is based on the consideration that observations made in particular organisms will provide insight into the workings of other, more complex systems. This volume is therefore a comprehensive and extensive collection of these important medical parallels.

Concise Biostatistical Principles And Concepts

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Author : Laurens Holmes, Jr
language : en
Publisher: Laurens Holmes, Jr
Release Date : 2025-03-18

Concise Biostatistical Principles And Concepts written by Laurens Holmes, Jr and has been published by Laurens Holmes, Jr this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Medical categories.

Concise Biostatistical Principles and Concepts, 2nd Edition 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. The four past decades have experienced modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery. While the application of novel statistical techniques to our data is necessary and fundamental to research, the selection of a sample and sampling method that reflects the representativeness of that sample to the targeted population is even more important. Since one of the rationale behind research conduct is to generate new knowledge and apply it to improve life situations including the improvement of patient and population health, sampling, sample size, and power estimations remain the basis for such inference. With the essential relevance of sample and sampling technique to how we come to make sense of data, the design of the study transcends statistical technique, since no statistical tool no matter how sophisticated can correct the errors of sampling. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample, randomization process when applicable prior to the selection of the analytic tool. In addition, it stresses the importance of biological and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is a random sample. When studies are conducted without random samples as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value. However, in the absence of a random sample, the p-value can be applied to designs that utilize consecutive samples, and disease registries, since these samples reflect the population of interest, and hence representative sample, justifying inference and generalization. Essential to the selection of test statistics is the understanding of the scale of the measurement of the variables, especially the response, outcome or dependent variable, type of sample (independent or correlated), hypothesis, and normality assumption. In terms of the selection of statistical tests, this text is based on the scale of measurement (binary), type of sample (single, independent), and relationship (linear). For example, if the scale of measurement of the outcome variable is binary, repeated measure, and normality is not assumed, the repeated measure logistic regression model remains a feasible model for evidence discovery in using the independent variables to predict the repeated outcome. This book presents a simplified approach to evidence discovery by recommending the graphic illustration of data and normality test for continuous (ratio/interval scale) data prior to statistical test selection. Unlike current text in biostatistics, the approach taken to present these materials is very simple. First, this text uses applied statistics by illustrating what, when, where, and why a test is appropriate. Where a text violates the normality assumption, readers are presented with a non-parametric alternative. The rationale for the test is explained with a limited mathematical formula and is intended in order to stress the applied nature of biostatistics. Attempts have been made in this book to present the most commonly used statistical model in biomedical or clinical research. We believe since no book is complete to have covered the basics that will facilitate the understanding of scientific evidence discovery. We hope this book remains a useful guide, which is our intention in bridging the gap between theoretical statistical models and reality in the statistical modeling of biomedical and clinical research data. As researchers we all make mistakes and we believe we have learned from our mistakes during the past three decades hence the need to examine flaws and apply reality in the statistical modeling of biomedical and research data. We hope this text results in increased reliability in the conduct, analysis,

Statistical Modeling For Biological Systems

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Author : Anthony Almudevar
language : en
Publisher: Springer Nature
Release Date : 2020-03-11

Statistical Modeling For Biological Systems written by Anthony Almudevar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-11 with Medical categories.

This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community.

Statistical Models And Methods For Biomedical And Technical Systems

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Author : Filia Vonta
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-05

Statistical Models And Methods For Biomedical And Technical Systems written by Filia Vonta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-05 with Medical categories.

This book deals with the mathematical aspects of survival analysis and reliability as well as other topics, reflecting recent developments in the following areas: applications in epidemiology; probabilistic and statistical models and methods in reliability; models and methods in survival analysis, longevity, aging, and degradation; accelerated life models; quality of life; new statistical challenges in genomics. The work will be useful to a broad interdisciplinary readership of researchers and practitioners in applied probability and statistics, industrial statistics, biomedicine, biostatistics, and engineering.

Statistical Modeling In Biomedical Research

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