Statistical Methods For Dynamic Disease Screening And Spatio Temporal Disease Surveillance
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Statistical Methods For Dynamic Disease Screening And Spatio Temporal Disease Surveillance
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Author : Peihua Qiu
language : en
Publisher: CRC Press
Release Date : 2024-06-18
Statistical Methods For Dynamic Disease Screening And Spatio Temporal Disease Surveillance written by Peihua Qiu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-18 with Mathematics categories.
Disease screening and disease surveillance (DSDS) constitute two critical areas in public health, each presenting distinctive challenges primarily due to their sequential decision-making nature and complex data structures. Statistical Methods for Dynamic Disease Screening and Spatio-Temporal Disease Surveillance explores numerous recent analytic methodologies that enhance traditional techniques. The author, a prominent researcher specializing in innovative sequential decision-making techniques, demonstrates how these novel methods effectively address the challenges of DSDS. After a concise introduction that lays the groundwork for comprehending the challenges inherent in DSDS, the book delves into fundamental statistical concepts and methods relevant to DSDS. This includes exploration of statistical process control (SPC) charts specifically crafted for sequential decision-making purposes. The subsequent chapters systematically outline recent advancements in dynamic screening system (DySS) methods, fine-tuned for effective disease screening. Additionally, the text covers both traditional and contemporary analytic methods for disease surveillance. It further introduces two recently developed R packages designed for implementing DySS methods and spatio-temporal disease surveillance techniques pioneered by the author's research team. Features • Presents Recent Analytic Methods for DSDS: The book introduces analytic methods for DSDS based on SPC charts. These methods effectively utilize all historical data, accommodating the complex data structure inherent in sequential decision-making processes. • Introduces Recent R Packages: Two recent R packages, DySS and SpTe2M, are introduced. The book not only presents these packages but also demonstrates key DSDS methods using them. • Examines Recent Research Results: The text delves into the latest research findings across various domains, including dynamic disease screening, nonparametric spatio-temporal data modeling and monitoring, and spatio-temporal disease surveillance. • Accessible Description of Methods: Major methods are described in a manner accessible to individuals without advanced knowledge in mathematics and statistics. The goal is to facilitate a clear understanding of ideas and easy implementation. • Real-Data Examples: To aid comprehension, the book provides several real-data examples illustrating key concepts and methods. • Hands-on Exercises: Each chapter includes exercises to encourage hands-on practice, allowing readers to engage directly with the presented methods.
Statistical Methods For Dynamic Disease Screening And Spatio Temporal Disease Surveillance
DOWNLOAD
Author : Peihua Qiu
language : en
Publisher: CRC Press
Release Date : 2024-06-18
Statistical Methods For Dynamic Disease Screening And Spatio Temporal Disease Surveillance written by Peihua Qiu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-18 with Mathematics categories.
Disease screening and disease surveillance (DSDS) constitute two critical areas in public health, each presenting distinctive challenges primarily due to their sequential decision-making nature and complex data structures. Statistical Methods for Dynamic Disease Screening and Spatio-Temporal Disease Surveillance explores numerous recent analytic methodologies that enhance traditional techniques. The author, a prominent researcher specializing in innovative sequential decision-making techniques, demonstrates how these novel methods effectively address the challenges of DSDS. After a concise introduction that lays the groundwork for comprehending the challenges inherent in DSDS, the book delves into fundamental statistical concepts and methods relevant to DSDS. This includes exploration of statistical process control (SPC) charts specifically crafted for sequential decision-making purposes. The subsequent chapters systematically outline recent advancements in dynamic screening system (DySS) methods, fine-tuned for effective disease screening. Additionally, the text covers both traditional and contemporary analytic methods for disease surveillance. It further introduces two recently developed R packages designed for implementing DySS methods and spatio-temporal disease surveillance techniques pioneered by the author's research team. Features • Presents Recent Analytic Methods for DSDS: The book introduces analytic methods for DSDS based on SPC charts. These methods effectively utilize all historical data, accommodating the complex data structure inherent in sequential decision-making processes. • Introduces Recent R Packages: Two recent R packages, DySS and SpTe2M, are introduced. The book not only presents these packages but also demonstrates key DSDS methods using them. • Examines Recent Research Results: The text delves into the latest research findings across various domains, including dynamic disease screening, nonparametric spatio-temporal data modeling and monitoring, and spatio-temporal disease surveillance. • Accessible Description of Methods: Major methods are described in a manner accessible to individuals without advanced knowledge in mathematics and statistics. The goal is to facilitate a clear understanding of ideas and easy implementation. • Real-Data Examples: To aid comprehension, the book provides several real-data examples illustrating key concepts and methods. • Hands-on Exercises: Each chapter includes exercises to encourage hands-on practice, allowing readers to engage directly with the presented methods.
Likelihood Methods In Survival Analysis
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Author : Jun Ma
language : en
Publisher: CRC Press
Release Date : 2024-10-01
Likelihood Methods In Survival Analysis written by Jun Ma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-01 with Mathematics categories.
Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes. Features Provides a broad and accessible overview of likelihood methods in survival analysis Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks Includes many examples using real data to illustrate the methods Includes integrated R code for implementation of the methods Supplemented by a GitHub repository with datasets and R code The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.
Biostatistics For Bioassay
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Author : Ann Yellowlees
language : en
Publisher: CRC Press
Release Date : 2024-12-24
Biostatistics For Bioassay written by Ann Yellowlees and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-24 with Mathematics categories.
In recent decades, there has been enormous growth in biologics research and development, with the accompanying development of biological assays for emerging products. In parallel, there have been substantial advances in statistical methodology, as well as technological advances in computer power, enabling new techniques to be implemented via statistical software. Biostatistics for Bioassay presents an overview of the statistical analysis techniques that are needed in order to report the results of biological assays. These assays are needed for testing all biological medicines, such as vaccines and cell therapies, to allow them to be released for use. Beginning with consideration of the performance characteristics required of a bioassay, including accuracy, precision, and combinations of these two attributes, the book builds a framework for statistical bioassay design. Features: Explains the statistical methods needed at each stage of the lifecycle of a bioassay Describes the demonstration of the bioassay’s performance, known as validation Covers the statistical techniques for monitoring the bioassay’s performance over time Details how to transfer the bioassay to another laboratory or replace critical reagents Provides examples at every stage, to allow the reader to work through the techniques and consolidate their understanding The book provides a resource for interested bioassay analysts, and statisticians working with bioassays. In bringing together best practices in statistics across the bioassay lifecycle into a single volume, it aims to provide a comprehensive and useful textbook for statistical analysis in bioassay.
Cluster Randomization Trials
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Author : Sin-Ho Jung
language : en
Publisher: CRC Press
Release Date : 2024-12-20
Cluster Randomization Trials written by Sin-Ho Jung and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Mathematics categories.
Oftentimes, small groups (called clusters) of individuals (called subunits) are randomized between treatment arms. Typically, clusters are families, classes, communities, surgeons operating patients, and so on. Such trials are called cluster randomization trials (CRTs). The subunits in each cluster share common frailties so that their outcomes tend to be positively correlated. Since clusters are independent, the data in two arms are independent in CRTs. In a clinical trial, multiple sites (such as teeth or ears) from each subject may be randomized between different treatment arms. In this case, the sites (subunits) of each subject (cluster) share common genetic, physiological, or environmental characteristics so that their observations tend to be positively correlated. This kind of trials are called subunit randomization trials (SRTs). In SRTs, dependency exists both within and between treatment arms. Individually randomized group treatment (IRGT) trials are composite of traditional independent subject randomization and CRTs. In an IRGT trial, the control arm is to treat patients individually, whereas the experimental arm is to treat patients using a group training, education, or treatment to increase the treatment effect by close interactions among patients. As a result, the outcome data of the control arm are independent as in traditional trials, but those in the experimental arm are correlated within each group (cluster) as in CRTs. Hence, two arms in IRGT trials have different dependency structures. Unlike standard CRTs, clusters of IRGT trials are usually organized after randomization. But statistically, they have identical statistical issues between the two types of trials, i.e., accounting for the dependency within each cluster. Although this book is entitled Cluster Randomization Trials, it covers all three types of trials (i.e., CRTs, SRTs, and IRGT trials) resulting in clustered data. For outcome variables of binary, continuous, and time-to-event types, we investigate generalized estimating equation type statistical tests and their sample size formulas. Also presented are random number generation algorithms for different types of outcome variables and randomization methods. The methods are discussed in terms of clinical trials, but can be used to design and analyze any types of experiments involving clustered data. This book also discusses statistical methods for various types of biomarker studies, including ROC methods, with clustered data. Key Features: Includes extensive statistical tests and their sample size formulas for various types of clinical trials resulting in clustered data. Handles different variable types of endpoints separately. Discusses algorithms to generate clustered binary and survival data that are useful for simulations. Covers statistical tests and sample size formulas for medical tests with clustered data.
R For Health Technology Assessment
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Author : Gianluca Baio
language : en
Publisher: CRC Press
Release Date : 2025-06-30
R For Health Technology Assessment written by Gianluca Baio and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Mathematics categories.
R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA). It has been designed with the objective of establishing the use of R as the standard tool for HTA amongst academics, industry practitioners and regulators. It covers a lot of ground, starting with the necessary background in HTA, R and statistical inference, followed by various modelling tools, ranging from missing data, survival analysis and decision trees, through to multistate models and discrete event simulation. The methods are all illustrated with many detailed worked examples and case studies using real data, and there are detailed descriptions of the code and processes. Key Features: Introductory chapters on the various topics of the book, including HTA, R and statistical inference A wide range of common analytical tools used in HTA, from modelling for individual-level data, missing data, survival analysis, decision-modelling and network meta-analysis More advanced and increasingly popular tools, such as those for population adjustment, discrete event simulation and the use of web applications as front-end for the overall statistical modelling Many detailed worked examples and case studies using real data to illustrate the methodology Fully integrated R code gives detailed guidance on implementation of the techniques Supplemented by a website with additional resources, including annotated code and data This text is primarily aimed at modellers working in the field of HTA, regulators and reviewers of reimbursement dossiers and cost-effectiveness analyses. It also complements a wide range of undergraduate and graduate programmes in HTA, health and public health economics, as well as academic researchers in the field of statistical modelling for HTA.
Power And Sample Size In R
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Author : Catherine M. Crespi
language : en
Publisher: CRC Press
Release Date : 2025-02-06
Power And Sample Size In R written by Catherine M. Crespi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-06 with Mathematics categories.
Power and Sample Size in R guides the reader through power and sample size calculations for a wide variety of study outcomes and designs and illustrates their implementation in R software. It is designed to be used as a learning tool for students as well as a resource for experienced statisticians and investigators. The book begins by explaining the process of power calculation step by step at an introductory level and then builds to increasingly complex and varied topics. For each type of study design, the information needed to perform a calculation and the factors that affect power are explained. Concepts are explained with statistical rigor but made accessible through intuition and examples. Practical advice for performing sample size and power calculations for real studies is given throughout. The book demonstrates calculations in R. It is integrated with the companion R package powertools and also draws on and summarizes the capabilities of other R packages. Only a basic proficiency in R is assumed. Topics include comparison of group means and proportions; ANOVA, including multiple comparisons; power for confidence intervals; multistage designs; linear, logistic and Poisson regression; crossover studies; multicenter, cluster randomized and stepped wedge designs; and time to event outcomes. Chapters are also devoted to designing noninferiority, superiority by a margin and equivalence studies and handling multiple primary endpoints. By emphasizing statistical thinking about the factors that influence power for different study designs and outcomes as well as providing R code, this book equips the reader with the knowledge and tools to perform their own calculations with confidence. Key Features: Explains power and sample size calculation for a wide variety of study designs and outcomes Suitable for both students and experienced researchers Highlights key factors influencing power and provides practical tips for designing real studies Includes extensive examples with R code
Clinical Trial Modernization
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Author : Harry Yang
language : en
Publisher: CRC Press
Release Date : 2025-05-26
Clinical Trial Modernization written by Harry Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-26 with Mathematics categories.
As the pharmaceutical industry navigates this new era of technological innovation, the integration of AI, big data, and advanced analytics into clinical trials holds immense potential to transform drug development. Clinical Trial Modernization: Technological, Operational, and Regulatory Advances provides a comprehensive overview of the current trends, challenges, and opportunities in modernizing clinical trials, offering a roadmap for stakeholders in this evolving field. This book serves as a valuable resource for professionals, researchers, and regulators, providing actionable insights into the future of clinical trials and their critical role in bringing new therapies to market faster and more effectively.
Applied Microbiome Statistics
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Author : Yinglin Xia
language : en
Publisher: CRC Press
Release Date : 2024-07-22
Applied Microbiome Statistics written by Yinglin Xia and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Mathematics categories.
This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts. Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research. Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data. Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity. Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data. Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot. Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration. Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used. Remarks on the advantages and disadvantages of each of the methods used.
Development Of Gene Therapies
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Author : Avery McIntosh
language : en
Publisher: CRC Press
Release Date : 2024-05-23
Development Of Gene Therapies written by Avery McIntosh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-23 with Mathematics categories.
Cell and gene therapies have become the third major drug modality in pharmaceutical medicine of the 21st century after low molecular weight and antibody drugs. The gene therapy (GTx) field is rapidly advancing, and yet there are still fundamental scientific questions that remain to be answered. Development of GTx products poses unique challenges and opportunities for drug developers. However, there is lack of a systematic exposition of the GTx product development and the pivotal role of the biostatistician in this process. Development of Gene Therapies: Strategic, Scientific, and Regulatory, and Access Considerations attempts to summarize the current state-of-the-art strategic, scientific, statistical, and regulatory aspects of GTx development. Intended to provide an exposition to the GTx new product development through peer-reviewed papers written by subject matter experts in this emerging field, this book will be useful for researchers in gene therapy drug development, biostatisticians, regulators, patient advocates, graduate students, and the finance and business development community . Key Features: A collection of papers covering a wide spectrum of topics in gene therapies (GTx), written by leading subject matter experts An exposition of the core principles of GTx product development, emerging business models, industry standards, best practices, and regulatory pathways An exposition of statistical and innovative modeling tools for design and analysis of clinical trials of GTx Insights into commercial models, access hurdles, and health economics of gene therapies Case studies of successful GTx approvals from core team members that developed the first two FDA-approved AAV gene therapies: Luxturna and Zolgensma A discussion of potential benefits and hurdles to be overcome for GTx in coming years from a multi-stakeholder perspective