Search

This book deals with the first discriminant theory of linearly separable data (LSD), Theory3, based on the four ordinary LSD of Theory1 and 169 microarrays (LSD) of Theory2. Furthermore, you can quickly analyze the medical data with the misclassified patients which is the true purpose of diagnoses. Author developed RIP (Optimal-linear discriminant function finding the combinatorial optimal solution) as Theory1 in decades ago, that found the minimum misclassifications. RIP discriminated 63 (=26−1) models of Swiss banknote (200•6) and found the minimum LSD: basic gene set (BGS). In Theory2, RIP discriminated Shipp microarray (77•7129) which was LSD and had only 32 nonzero coefficients (first Small Matryoshka; SM1). Because RIP discriminated another 7,097 genes and found SM2, the author developed the Matryoshka feature selection Method 2 (Program 3), that splits microarray into many SMs. Program4 can split microarray into many BGSs. Then, the wide columnLSD (Revolution-0), such as microarray (n

This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential'on-line'methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises.Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall,'Change Point Analysis for Time Series'offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data.

Scan statistics, one of the most active research areas in applied probability and statistics, has seen a tremendous growth during the last 25 years. Google Scholar lists about 3,500 hits to references of articles on scan statistics since the year 2020, resulting in over 850 hits to articles per year. This is mainly due to extensive and diverse areas of science and technology where scan statistics have been employed, including: atmospheric and climate sciences, business, computer science, criminology, ecology, epidemiology, finance, genetics and genomics, geographic sciences, medical and health sciences, nutrition, pharmaceutical sciences, physics, quality control and reliability, social networks and veterinary science. This volume of the Handbook of Scan Statistics is a collection of forty chapters, authored by leading experts in the field, outlines the research and the breadthof applications of scan statistics to the numerous areas of science and technology listed above. These chapters present an overview of the theory, methods and computational techniques, related to research in the area of scan statistics and outline future developments. It contains extensive references to research articles, books and relevant computer software. Handbook of Scan Statistics is an excellent reference for researchers and graduate students in applied probability and statistics, as well as for scientists in research areas where scan statistics are used. This volume may also be used as a textbook for a graduate level course on scan statistics.

This book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion parameters. The new estimating equations in these two cases are generated by maximizing a'likelihood'function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessianmatrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models.Standard GEE, partially modified GEE, fully modified GEE, and ELMM are demonstrated and compared using a variety of regression analyses of different types of correlated outcomes. Example analyses of correlated outcomes include linear regression for continuous outcomes, Poisson regression for count/rate outcomes, logistic regression for dichotomous outcomes, exponential regression for positive-valued continuous outcome, multinomial regression for general polytomous outcomes, ordinal regression for ordinal polytomous outcomes, and discrete regression for discrete numeric outcomes. These analyses also address nonlinearity in predictors based on adaptive search through alternative fractional polynomial models controlled by likelihood cross-validation (LCV) scores. Larger LCV scores indicate better models but not necessarilydistinctly better models. LCV ratio tests are used to identify distinctly better models.A SAS macro has been developed for analyzing correlated outcomes using standard GEE, partially modified GEE, fully modified GEE, and ELMM within alternative regression contexts. This macro and code for conducting the analyses addressed in the book are available online via the book's Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book.

Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term'data science'and discusses the many professional skills and competencies affiliated with the industry. With data science being a leading indicator of interest in STEM fields, the text also investigates this ongoing growth of demand in these spaces, with the goal of providing readers who are entering the professional world with foundational knowledge of required skills, job trends, and salary expectations. The text provides a historical overview of computing and the field's progression to R as it exists today, including the multitude of packages and functions associated with both Base R and the tidyverse ecosystem. Readers will learn how to use R to work with real data, as well as how to communicate results to external stakeholders. A distinguishing feature of this text is its emphasis on the emerging use of APIs to obtain data.

This comprehensive book covers a wide variety of methods for estimating the sizes and related parameters of closed populations. With the effect of climate change, and human territory invasion, we have seen huge species losses and a major biodiversity decline. Populations include plants, trees, various land and sea animals, and some human populations. With such a diversity of populations, an extensive variety of different methods are described with the collection of different types of data. For example, we have count data from plot sampling, which can also allow for incomplete detection. There is a large chapter on occupancy methods where a major interest is determining whether a particular species is present or not. Citizen and opportunistic survey data can also be incorporated. A related topic is species methods, where species richness and species'interactions are of interest. A variety of distance methods are discussed. One can use distances from points and lines, as wellas nearest neighbor distances. The applications are extensive, and include marine, acoustic, and aerial surveys, using multiple observers or detection devices. Line intercept measurements have a role to play such as, for example, estimating parameters relating to plant coverage. An increasingly important class of removal methods considers successive “removals'from a population, with physical removal or'removal'by capture-recapture of marked individuals. With the change-in-ratio method, removals are taken from two or more classes, e.g., males and females. Effort data used for removals can also be used. A very important method for estimating abundance is the use of capture-recapture data collected discretely or continuously and can be analysed using both frequency and Bayesian methods. Computational aspects of fitting Bayesian models are described. A related topic of growing interest is the use of spatial and camera methods. With the plethora of models there has been a corresponding development of various computational methods and packages, which are often mentioned throughout. Covariate data is being used more frequently, which can reduce the number of unknown parameters by using logistic and loglinear models. An important computational aspect is that of model selection methods. The book provides a useful list of over 1400 references.

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.

This book covers methods of applied statistics for researchers who design and conduct experiments, perform statistical inference, and write technical reports. These research activities rely on an adequate knowledge of applied statistics. The reader both builds on basic statistics skills and learns to apply it to applicable scenarios without over-emphasis on the technical aspects. Demonstrations are a very important part of this text. Mathematical expressions are exhibited only if they are defined or intuitively comprehensible.This text may be used as a guidebook for applied researchers or as an introductory statistical methods textbook for students, not majoring in statistics. Discussion includes essential probability models, inference of means, proportions, correlations and regressions, methods for censored survival time data analysis, and sample size determination.

This book addresses the description and analysis of occurrence data frequently encountered in epidemiological studies. With the occurrence of Covid-19, people have been exposed to the analysis and interpretation of epidemiological data. To be informed consumers of this information, people need to understand the nature and analysis of these data. Effort is made to emphasize concepts rather than mathematics. Subjects range from description of the frequencies of disease to the analysis of associations between the occurrence of disease and exposure. Those analyses begin with simple associations and work up to complex relationships that involve the control of extraneous characteristics. Analyses rely on the statistical software R, which is freeware in wide use by professional epidemiologists and other scientists.

This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area. It presents the common concepts and techniques for linear and nonlinear models as well as Bayesian optimal designs. The last two chapters are devoted to particular themes of interest, including recent developments and hot topics in optimal experimental design, and real-world applications. Numerous examples and exercises are included, some of them with solutions or hints, as well as references to the existing software for computing designs. The book is primarily intended for graduate students and young researchers in statistics and applied mathematics who are new to the field of optimal experimental design. Given the applications and the way concepts and results are introduced, parts of the text will also appeal to engineers and other applied researchers.

This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.

This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step.This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory.Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions.Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.

Dieses essential zeigt die korrekte Anwendung von Konfidenzintervallen und hilft, Fehlinterpretationen derselben zu vermeiden bzw. zu erkennen. Auf die mathematischen Tiefen der Statistik wird bewusst verzichtet. Leser lernen in diesem essential den Begriff und die Bedeutung des Konfidenzintervalls im Kontext der dahinterstehenden Idee von Jerzy Neyman (1894-1981) kennen. Beispiele und Abbildungen erleichtern das Erfassen des Konzepts Konfidenzintervall.

The SIR - model supported by a new density and its derivatives receive a statistical data background from frequency distributions, from whose parameter values over the new density distribution a quality-oriented probability of the respective infection process and its future can be concluded. Thus the COVID - management receives a functionally model basis for the preventive control of the components time planning, cost development, quality management and personnel and material employment.

This book is designed as a textbook for graduate students and as a resource for researchers seeking a thorough mathematical treatment of its subject. It develops the main results of regression and the analysis of variance, as well as the central results on confounded and fractional factorial experiments. Matrix theory is deemphasized; its role is taken instead by the theory of linear transformations between vector spaces.The text gives a carefully paced and unified presentation of two topics, linear models and experimental design. Students are assumed to have a solid background in linear algebra, basic knowledge of regression and analysis of variance, and some exposure to experimental design, and should be comfortable with reading and constructing mathematical proofs.The book leads students into the mathematical theory, including many examples both for motivation and for illustration. Over 130 exercises of varying difficulty are included. An extensive mathematical appendix and a detailed index make the text especially accessible.Linear Models and Design can serve as a textbook for a year-long course in the topics covered, or for a one-semester course in either linear model theory or experimental design. It prepares students for more advanced topics in the field, and assists in developing a thoughtful approach to the existing literature. It includes a guide to terminology as well as discussion of the history and development of ideas, and offers a fresh perspective on the fundamental concepts and results of the subject.

This book focuses on multiple comparisons of proportions in multi-sample models with Bernoulli responses. First, the author explains the one-sample and two-sample methods that form the basis of multiple comparisons. Then, regularity conditions are stated in detail. Simultaneous inference for all proportions based on exact confidence limits and based on asymptotic theory is discussed. Closed testing procedures based on some one-sample statistics are introduced. For all-pairwise multiple comparisons of proportions, the author uses arcsine square root transformation of sample means. Closed testing procedures based on maximum absolute values of some two-sample test statistics and based on chi-square test statistics are introduced. It is shown that the multi-step procedures are more powerful than single-step procedures and the Ryan–Einot–Gabriel–Welsch (REGW)-type tests. Furthermore, the author discusses multiple comparisons with a control. Under simple ordered restrictions of proportions, the author also discusses closed testing procedures based on maximum values of two-sample test statistics and based on Bartholomew's statistics. Last, serial gatekeeping procedures based on the above-mentioned closed testing procedures are proposed although Bonferroni inequalities are used in serial gatekeeping procedures of many.

The first half of the book is aimed at quantitative research workers in biology, medicine, ecology and genetics. The book as a whole is aimed at graduate students in statistics, biostatistics, and other quantitative disciplines. Ten detailed examples show how the author approaches real-world statistical problems in a principled way that allows for adequate compromise and flexibility. The need to accommodate correlations associated with space, time and other relationships is a recurring theme, so variance-components models feature prominently. Statistical pitfalls are illustrated via examples taken from the recent scientific literature. Chapter 11 sets the scene, not just for the second half of the book, but for the book as a whole. It begins by defining fundamental concepts such as baseline, observational unit, experimental unit, covariates and relationships, randomization, treatment assignment, and the role that these play in model formulation. Compatibility of the model with the randomization scheme is crucial. The effect of treatment is invariably modelled as a group action on probability distributions. Technical matters connected with space-time covariance functions, residual likelihood, likelihood ratios, and transformations are discussed in later chapters.

Dieses Buch stellt eine aktuelle Auswahl mathematisch-statistischer Methoden und Stichprobenstrategien zum Umgang mit kontinuierlichen Messgrößen in Raum und Zeit dar. Es unterstützt beispielsweise bei der Erfassung, Darstellung, Beurteilung und statistischen Auswertung von Messsignalen sowie bei der Entwicklung und Ausgestaltung von statistischen Analyseverfahren und geeigneten Stichprobenplänen. Die Buchinhalte sind insbesondere zur Anwendung in den Natur- und Umweltwissenschaften geeignet, da dort kontinuierliche Messgrößen in Raum und Zeit besonders häufig auftreten, die damit verbundenen kontinuierlichen Prozesse aber meist nur stichprobenhaft an einigen Stellen bzw. Zeitpunkten beobachtet werden können und zudem oftmals mit Fehlern behaftet sind. Spezielle Kapitel (z.B. Komponentenmodelle) sind auch für Wirtschafts- und Finanzwissenschaftler (Chartanalyse) von Interesse.

Das durch eine neue Dichte unterstützte SIR – Modell und dessen Derivate erhalten einen statistischen Datenhintergrund aus Häufigkeitsverteilungen, aus deren Parameterwerten über die neue Dichteverteilung auf eine qualitätsorientierte Wahrscheinlichkeit des jeweiligen Infektionsprozesses und seiner Zukunft geschlossen werden kann. Dadurch erhält das COVID - Management eine funktionsgemäße modellhafte Grundlage zur vorbeugenden Steuerung der Komponenten Zeitplanung, Kostenentwicklung, Qualitätsmanagement und Personal- und Materialeinsatz.

This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censored survival data can easily be mistaken for typical right-censored survival data, which can result in erroneous statistical inference due to the complexity of this type of data. The book invites a group of internationally leading researchers to systematically discuss and explore the historical development of the associated methods and their computational implementations, as well as emerging topics related to interval-censored data. It covers a variety of topics, including univariate interval-censored data, multivariate interval-censored data, clustered interval-censored data, competing risk interval-censored data, data with interval-censored covariates, interval-censored data from electric medical records, and misclassified interval-censored data. Researchers, students, and practitioners can directly make use of the state-of-the-art methods covered in the book to tackle their problems in research, education, training and consultation.

This volume presents a collection of twenty-five peer-reviewed articles carefully selected from the contributions presented at the XXV Congress of the Portuguese Statistical Society (2021). Containing state-of-the-art developments in theoretical and applied statistics, the book will be accessible to readers with a background in mathematics and statistics, but will also be of interest to researchers from other scientific disciplines (e.g., biology, economics, medicine), who will find a broad range of relevant applications.

A thorough treatment of the statistical methods used to analyze doubly truncated data In The Statistical Analysis of Doubly Truncated Data, an expert team of statisticians delivers an up-to-date review of existing methods used to deal with randomly truncated data, with a focus on the challenging problem of random double truncation. The authors comprehensively introduce doubly truncated data before moving on to discussions of the latest developments in the field. The book offers readers examples with R code along with real data from astronomy, engineering, and the biomedical sciences to illustrate and highlight the methods described within. Linear regression models for doubly truncated responses are provided and the influence of the bandwidth in the performance of kernel-type estimators, as well as guidelines for the selection of the smoothing parameter, are explored. Fully nonparametric and semiparametric estimators are explored and illustrated with real data. R code for reproducing the data examples is also provided. The book also offers: A thorough introduction to the existing methods that deal with randomly truncated data Comprehensive explorations of linear regression models for doubly truncated responses Practical discussions of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameter In-depth examinations of nonparametric and semiparametric estimators Perfect for statistical professionals with some background in mathematical statistics, biostatisticians, and mathematicians with an interest in survival analysis and epidemiology, The Statistical Analysis of Doubly Truncated Data is also an invaluable addition to the libraries of biomedical scientists and practitioners, as well as postgraduate students studying survival analysis.

This book presents modern methods and real-world applications of compositional data analysis. It covers a wide variety of topics, ranging from an updated presentation of basic concepts and ideas in compositional data analysis to recent advances in the context of complex data structures. Further, it illustrates real-world applications in numerous scientific disciplines and includes references to the latest software solutions available for compositional data analysis, thus providing a valuable and up-to-date guide for researchers and practitioners working with compositional data. Featuring selected contributions by leading experts in the field, the book is dedicated to Vera Pawlowsky-Glahn on the occasion of her 70th birthday.

The book describes the possibility of making a probabilistic prognosis, which uses the mean n-day logarithm of case numbers in the past to determine an exponent for a probability density for a prognosis, as well as the particle emission concept, which is derived from contact and distribution rates that increase the exponent of the probable development to the extent that a group of people can be formed.

In diesem essential wird die Anwendung von statistischen Analysen von normalverteilten Daten im Laboralltag dargestellt. Hierzu zählen die Berechnung von Mittelwert und Standardabweichung sowie die Erstellung von einfachen grafischen Abbildungen. Im Anschluss werden Tests auf Ausreißer und Normalverteilung sowie Stichprobenberechnungen anhand einfacher Beispiele vorgestellt. Abschließend erlernen die Leser•innen den Vergleich von Datengruppen mittels t-test und ANOVA, das Erkennen von Zusammenhängen mittels linearer Regression und Korrelation sowie die Anwendung von Konfidenzintervallen. Zusätzlich werden übliche Fehlerquellen bei der Anwendung statistischer Methoden erklärt.

Written for undergraduate and graduate students with little or no mathematical background, Biostatistics for Population Health: A Primer offers current and future health professionals a clear, and accessible approach to learning the basic tools and techniques necessary to conduct biostatistical analyses and the professional confidence to critically evaluate and interpret biostatistical findings. Each unit begins with a contemporary population health issue (e.g., the opioid crisis, physical inactivity among children, diabetes) and raises questions that require the use of techniques discussed in that unit. Each technique, in turn, is illustrated with realistic, contemporary examples (e.g. vaping) to pique student interest. By the end of the unit, students are encouraged to apply the techniques to address the questions that were raised.

Das Buch beschreibt die Möglichkeit, eine probabilistische Prognose zu erstellen, die den mittleren n-Tage-Logarithmus von Fallzahlen in der Vergangenheit verwendet, um einen Exponenten für eine Wahrscheinlichkeitsdichte für eine Prognose zu bestimmen, als auch das Partikelemissionskonzept, das sich herleitet aus Kontakt- und Verteilungsrate, welche den Exponenten der voraussichtlichen Entwicklung in dem Maß erhöht wie sich eine Gruppenbildung von Personen bilden kann.

This book volume provides complete and updated information on the applications of Design of Experiments (DoE) and related multivariate techniques at various stages of pharmaceutical product development. It discusses the applications of experimental designs that shall include oral, topical, transdermal, injectables preparations, and beyond for nanopharmaceutical product development, leading to dedicated case studies on various pharmaceutical experiments through illustrations, art-works, tables and figures. This book is a valuable guide for all academic and industrial researchers, pharmaceutical and biomedical scientists, undergraduate and postgraduate research scholars, pharmacists, biostatisticians, biotechnologists, formulations and process engineers, regulatory affairs and quality assurance personnel.

This book volume provides complete and updated information on the applications of Design of Experiments (DoE) and related multivariate techniques at various stages of pharmaceutical product development. It discusses the applications of experimental designs that shall include oral, topical, transdermal, injectables preparations, and beyond for nanopharmaceutical product development, leading to dedicated case studies on various pharmaceutical experiments through illustrations, art-works, tables and figures. This book is a valuable guide for all academic and industrial researchers, pharmaceutical and biomedical scientists, undergraduate and postgraduate research scholars, pharmacists, biostatisticians, biotechnologists, formulations and process engineers, regulatory affairs and quality assurance personnel.

Contains all you need to know to understand statistics in medicine. Medical Statistics Made Easy has been a perennial bestseller since the first edition was published (it is consistently a #1 bestseller in medical statistics/biostatistics on Amazon). It is recommended and required worldwide on a variety of courses and programmes, from undergraduate medicine, through to professional medical qualifications. It is a book of key statistics principles for anyone studying or working in medicine and healthcare who needs a basic overview of the subject. It is ideal for non-statisticians who need to understand how statistics are used and applied in medicine and medical research. Using a consistent format, the authors describe the most common statistical methods in turn and then rate them on how difficult they are to understand and how common they are. The worked examples that demonstrate the statistical method in action include current articles from the medical literature and now feature a wider range of medical journals. This fourth edition continues with the same structure as the previous editions, with new sections on cut-off points and ROC curves, as well as a new chapter on choosing the right statistical test. It also features a completely revised and updated'Statistics at work'section.

APPLIED BIOSTATISTICS FOR THE HEALTH SCIENCES In this newly revised edition of Applied Biostatistics for the Health Sciences, accomplished statistician Dr. Richard Rossi delivers a robust and easy-to-understand exploration of statistics in the context of applied health science and biostatistics. The book covers sample design, logistic regression, experimental design, survival analysis, basic statistical computation, and many more topics with a strong focus on the correct use and interpretation of statistics. The author also explains how to assess the quality of observed data, how to collect quality data, and the use of confidence intervals in conjunction with hypothesis and significance tests. A thorough introduction to biostatistics, including explanations of fundamental concepts like populations, samples, statistics, biomedical studies, and data set examples A comprehensive exploration of population descriptions, including qualitative and quantitative variables, multivariate data, measures of dispersion, and probability Practical discussions of random sampling, summarizing random samples, and the measurement of the reliability of statistics In-depth examinations of confidence intervals, statistical hypothesis testing, simple and multiple linear regression, and experimental design Perfect for health science and biostatistics students and professors at the upper undergraduate and graduate levels, Applied Biostatistics for the Health Sciences is also a must-read reference for practitioners and professionals in the fields of pharmacy, biochemistry, nursing, health care informatics, and the applied health sciences.

This book provides a guided approach to the geostatistical modelling of compositional spatial data. These data are data in proportions, percentages or concentrations distributed in space which exhibit spatial correlation. The book can be divided into four blocks. The first block sets the framework and provides some background on compositional data analysis. Block two introduces compositional exploratory tools for both non-spatial and spatial aspects. Block three covers all necessary facets of multivariate spatial prediction for compositional data: variogram modelling, cokriging and validation. Finally, block four details strategies for simulation of compositional data, including transformations to multivariate normality, Gaussian cosimulation, multipoint simulation of compositional data, and common postprocessing techniques, valid for both Gaussian and multipoint methods. All methods are illustrated via applications to two types of data sets: one a large-scale geochemical survey, comprised of a full suite of geochemical variables, and the other from a mining context, where only the elements of greatest importance are considered. R codes are included for all aspects of the methodology, encapsulated in the R package'gmGeostats', available in CRAN.

This book takes a unique approach to explaining permutation statistics by integrating permutation statistical methods with a wide range of classical statistical methods and associated R programs. It opens by comparing and contrasting two models of statistical inference: the classical population model espoused by J. Neyman and E.S. Pearson and the permutation model first introduced by R.A. Fisher and E.J.G. Pitman. Numerous comparisons of permutation and classical statistical methods are presented, supplemented with a variety of R scripts for ease of computation. The text follows the general outline of an introductory textbook in statistics with chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, completely-randomized analysis of variance, randomized-blocks analysis of variance, simple linear regression and correlation, and the analysis of goodness of fit and contingency. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity, depend only on the observed data, and do not require random sampling. The methods are relatively new in that it took modern computing power to make them available to those working in mainstream research. Designed for an audience with a limited statistical background, the book can easily serve as a textbook for undergraduate or graduate courses in statistics, psychology, economics, political science or biology. No statistical training beyond a first course in statistics is required, but some knowledge of, or some interest in, the R programming language is assumed.

This book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e.g., on handling missing values in PCA, multivariate process monitoring and batch correction).

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field.

This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal distribution models. More specifically, it presents recent techniques and results in estimation of mean and covariance matrices with a high-dimensional setting that implies singularity of the sample covariance matrix. Such high-dimensional models can be analyzed by using the same arguments as for low-dimensional models, thus yielding a unified approach to both high- and low-dimensional shrinkage estimations. The unified shrinkage approach not only integrates modern and classical shrinkage estimation, but is also required for further development of the field. Beginning with the notion of decision-theoretic estimation, this book explains matrix theory, group invariance, and other mathematical tools for finding better estimators. It also includes examples of shrinkage estimators for improving standard estimators, such as least squares, maximum likelihood, and minimum risk invariantestimators, and discusses the historical background and related topics in decision-theoretic estimation of parameter matrices. This book is useful for researchers and graduate students in various fields requiring data analysis skills as well as in mathematical statistics.

This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health. ​

The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences. Especially in the era of big data, researchers can easily collect data characterised by massive dimensions and complexity. In celebration of Professor Kai-Tai Fang's 80th birthday, we present this book, which furthers new and exciting developments in modern statistical theories, methods and applications. The book features four review papers on Professor Fang's numerous contributions to the fields of experimental design, multivariate analysis, data mining and education. It also contains twenty research articles contributed by prominent and active figures in their fields. The articles cover a wide range of important topics such as experimental design, multivariate analysis, data mining, hypothesis testing and statistical models.

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 researchfields 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.

Correctly understanding and using medical statistics is a key skill for all medical students and health professionals.In an informal and friendly style, Medical Statistics from Scratch provides a practical foundation for everyone whose first interest is probably not medical statistics. Keeping the level of mathematics to a minimum, it clearly illustrates statistical concepts and practice with numerous real-world examples and cases drawn from current medical literature. Medical Statistics from Scratch is an ideal learning partner for all medical students and health professionals needing an accessible introduction, or a friendly refresher, to the fundamentals of medical statistics.

Recent Statistical techniques are one of the basal evidence for clinical research, a pivotal in handling new clinical research and in evaluating and applying prior research. This book explores various choices of statistical tools and mechanisms, analyses of the associations among different clinical attributes. It uses advanced statistical methods to describe real clinical data sets, when the clinical processes being examined are still in the process. This book also discusses distinct methods for building predictive and probability distribution models in clinical situations and ways to assess the stability of these models and other quantitative conclusions drawn by realistic experimental data sets. Design of experiments and recent posthoc tests have been used in comparing treatment effects and precision of the experimentation. This book also facilitates clinicians towards understanding statistics and enabling them to follow and evaluate the real empirical studies (formulation of randomized control trial) that pledge insight evidence base for clinical practices. This book will be a useful resource for clinicians, postgraduates scholars in medicines, clinical research beginners and academicians to nurture high-level statistical tools with extensive scope.

This volume is a tribute to Professor Dietrich von Rosen on the occasion of his 65th birthday. It contains a collection of twenty original papers. The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis, goodness-of-fit measures, variable selection and information criteria, inference of covariance structures, the Wishart distribution and growth curve models.

Now in its fourth edition, Medical Statistics at a Glance is a concise and accessible introduction to this complex subject. It provides clear instruction on how to apply commonly used statistical procedures in an easy-to-read, comprehensive and relevant volume. This new edition continues to be the ideal introductory manual and reference guide to medical statistics, an invaluable companion for statistics lectures and a very useful revision aid.This new edition of Medical Statistics at a Glance: Offers guidance on the practical application of statistical methods in conducting research and presenting results Explains the underlying concepts of medical statistics and presents the key facts without being unduly mathematical Contains succinct self-contained chapters, each with one or more examples, many of them new, to illustrate the use of the methodology described in the chapter. Now provides templates for critical appraisal, checklists for the reporting of randomized controlled trials and observational studies and references to the EQUATOR guidelines for the presentation of study results for many other types of study Includes extensive cross-referencing, flowcharts to aid the choice of appropriate tests, learning objectives for each chapter, a glossary of terms and a glossary of annotated full computer output relevant to the examples in the text Provides cross-referencing to the multiple choice and structured questions in the companion Medical Statistics at a Glance Workbook Medical Statistics at a Glance is a must-have text for undergraduate and post-graduate medical students, medical researchers and biomedical and pharmaceutical professionals.

The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

This book provides an extensive overview of the principles and methods of sample size calculation and recalculation in clinical trials. Appropriate calculation of the required sample size is crucial for the success of clinical trials. At the same time, a sample size that is too small or too large is problematic due to ethical, scientific, and economic reasons. Therefore, state-of-the art methods are required when planning clinical trials. Part I describes a general framework for deriving sample size calculation procedures. This enables an understanding of the common principles underlying the numerous methods presented in the following chapters. Part II addresses the fixed sample size design, where the required sample size is determined in the planning stage and is not changed afterwards. It covers sample size calculation methods for superiority, non-inferiority, and equivalence trials, as well as comparisons between two and more than two groups. A wide range of further topics is discussed, including sample size calculation for multiple comparisons, safety assessment, and multi-regional trials. There is often some uncertainty about the assumptions to be made when calculating the sample size upfront. Part III presents methods that allow to modify the initially specified sample size based on new information that becomes available during the ongoing trial. Blinded sample size recalculation procedures for internal pilot study designs are considered, as well as methods for sample size reassessment in adaptive designs that use unblinded data from interim analyses. The application is illustrated using numerous clinical trial examples, and software code implementing the methods is provided. The book offers theoretical background and practical advice for biostatisticians and clinicians from the pharmaceutical industry and academia who are involved in clinical trials. Covering basic as well as more advanced and recently developed methods, it is suitable forbeginners, experienced applied statisticians, and practitioners. To gain maximum benefit, readers should be familiar with introductory statistics. The content of this book has been successfully used for courses on the topic.

This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community.

Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students'anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engagingexplanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/

This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided. All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias. The main attraction for readers is the book's simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results.

This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book “Applied Statistical Inference” has been expanded to include new material on Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis, and each chapter is complemented by exercises. The text is primarily intended for graduate statistics and biostatistics students with an interest in applications.

A good understanding of medical statistics is essential to evaluate medical research and to choose appropriate ways of implementing findings in clinical practice. The Oxford Handbook of Medical Statistics has been written to provide doctors and medical students with a comprehensive yet concise account of this often difficult subject. Described by readers as a'statistical Bible', this new edition maintains the accessibility and thoroughness of the original, and includes comprehensive updates including new sections on transitional medicine, cluster designs, and modern statistical packages. The Handbook promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t- and chi-squared tests, to complex multifactorial analyses, all using examples from published research. References and further reading are included, to allow deeper understanding on specific topics. Featuring a new chapter on how to use this book in different medical contexts, the Oxford Handbook of Medical Statistics helps readers to conduct their own research and critically appraise others'work.