Statistical Analysis of Data with a Dense Sequence of Measurements from Medical Devices for Evaluating Subclinical Disease PDF Download
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Author: Michelle J. Keyes
Publisher:
ISBN: 9781109976199
Category :
Languages : en
Pages : 135
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Book Description
Of interest are the change and percent change from baseline when pre- and postmeasurements are made. Researchers commonly use OLS regression (OLS) on percent change variables versus baseline; sometimes this violates the assumption of homoscedasticity. Simulations are performed to compare several regression methods to OLS, including WLS regression, as well as unweighted and weighted non-linear regression. If the pre- and post-measurements are highly correlated or linearly related, percent change will be a function of pre-measurement values.
Author: Michelle J. Keyes
Publisher:
ISBN: 9781109976199
Category :
Languages : en
Pages : 135
Get Book Here
Book Description
Of interest are the change and percent change from baseline when pre- and postmeasurements are made. Researchers commonly use OLS regression (OLS) on percent change variables versus baseline; sometimes this violates the assumption of homoscedasticity. Simulations are performed to compare several regression methods to OLS, including WLS regression, as well as unweighted and weighted non-linear regression. If the pre- and post-measurements are highly correlated or linearly related, percent change will be a function of pre-measurement values.
Author: Scott A. Pardo
Publisher: Springer Nature
ISBN: 3031261399
Category : Mathematics
Languages : en
Pages : 384
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Book Description
This book provides a reference for people working in the design, development, and manufacturing of medical devices. While there are no statistical methods specifically intended for medical devices, there are methods that are commonly applied to various problems in the design, manufacturing, and quality control of medical devices. The aim of this book is not to turn everyone working in the medical device industries into mathematical statisticians; rather, the goal is to provide some help in thinking statistically, and knowing where to go to answer some fundamental questions, such as justifying a method used to qualify/validate equipment, or what information is necessary to support the choice of sample sizes. While, there are no statistical methods specifically designed for analysis of medical device data, there are some methods that seem to appear regularly in relation to medical devices. For example, the assessment of receiver operating characteristic curves is fundamental to development of diagnostic tests, and accelerated life testing is often critical for assessing the shelf life of medical device products. Another example is sensitivity/specificity computations are necessary for in-vitro diagnostics, and Taguchi methods can be very useful for designing devices. Even notions of equivalence and noninferiority have different interpretations in the medical device field compared to pharmacokinetics. It contains topics such as dynamic modeling, machine learning methods, equivalence testing, and experimental design, for example. This book is for those with no statistical experience, as well as those with statistical knowledgeable—with the hope to provide some insight into what methods are likely to help provide rationale for choices relating to data gathering and analysis activities for medical devices.
Author: Margaret Sullivan Pepe
Publisher: OUP Oxford
ISBN: 019158861X
Category : Medical
Languages : en
Pages : 319
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Book Description
This book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine.
Author: Bailar/Mostelle
Publisher: CRC Press
ISBN: 1482255626
Category : Mathematics
Languages : en
Pages : 478
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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: Pentti Nieminen
Publisher: MDPI
ISBN: 3036500987
Category : Medical
Languages : en
Pages : 250
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Book Description
During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role.
Author: Brian Everitt
Publisher: Springer Science & Business Media
ISBN: 1475732856
Category : Computers
Languages : en
Pages : 488
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Book Description
Each chapter consists of basic statistical theory, simple examples of S-PLUS code, plus more complex examples of S-PLUS code, and exercises. All data sets are taken from genuine medical investigations and will be available on a web site. The examples in the book contain extensive graphical analysis to highlight one of the prime features of S-PLUS. Written with few details of S-PLUS and less technical descriptions, the book concentrates solely on medical data sets, demonstrating the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.
Author: Ying Lu
Publisher: World Scientific
ISBN: 9789810248000
Category : Medical
Languages : en
Pages : 1118
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Book Description
This book presents new and powerful advanced statistical methods that have been used in modern medicine, drug development, and epidemiology. Some of these methods were initially developed for tackling medical problems. All 29 chapters are self-contained. Each chapter represents the new development and future research topics for a medical or statistical branch. For the benefit of readers with different statistical background, each chapter follows a similar style: the explanation of medical challenges, statistical ideas and strategies, statistical methods and techniques, mathematical remarks and background and reference. All chapters are written by experts of the respective topics.
Author: Don Hong
Publisher: World Scientific
ISBN: 9814476234
Category : Medical
Languages : en
Pages : 364
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Book Description
Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis.This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing and statistical computing.
Author: Jeffrey Wilson
Publisher: CRC Press
ISBN: 1000848825
Category : Business & Economics
Languages : en
Pages : 280
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Book Description
This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data, the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research.
Author: Albert Vexler
Publisher: CRC Press
ISBN: 1315353016
Category : Mathematics
Languages : en
Pages : 622
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Book Description
Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method, with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept, providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches, this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies, including clinical trials. Theoretical statisticians, medical researchers, and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics, epidemiology, health-related sciences, and areas pertaining to formal decision-making mechanisms.
