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Statistical Methods for Overdispersed Count Data

Statistical Methods for Overdispersed Count Data
A Book

by Jean-Francois Dupuy

  • Publisher : Elsevier
  • Release : 2018-11-19
  • Pages : 192
  • ISBN : 008102374X
  • Language : En, Es, Fr & De
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Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner. Includes reading on several levels, including methodology and applications Presents the state-of-the-art on the most recent zero-inflated regression models Contains a single dataset that is used as a common thread for illustrating all methodologies Includes R code that allows the reader to apply methodologies

Statistical Methods for Modeling Count Data with Overdispersion and Missing Time Varying Categorical Covariates

Statistical Methods for Modeling Count Data with Overdispersion and Missing Time Varying Categorical Covariates
A Book

by Elizabeth H. Payne

  • Publisher : Unknown Publisher
  • Release : 2016
  • Pages : 296
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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A Comparison of Statistical Models for Correlated Over-dispersed Count Data

A Comparison of Statistical Models for Correlated Over-dispersed Count Data
A Book

by Elizabeth Anne Wynn

  • Publisher : Unknown Publisher
  • Release : 2018
  • Pages : 68
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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As the cost of RNA-sequencing (RNA-Seq) decreases, it becomes increasingly feasible to collect RNA-Seq data under complex study designs, including paired, longitudinal, and other correlated designs. Commonly used RNA-Seq analysis tools do not allow for correlation between observations, which is common in these types of studies. When applying statistical methods with mechanisms to account for correlated data to RNA-Seq experiments, extra considerations must be made because RNA-Seq experiments include data on 10,000 to 20,000 genes, resulting in a large number of statistical models and tests. Thus, in this setting achieving model convergence for all genes and maintaining nominal type 1 error and false discovery rates can be problematic. Furthermore, RNA-Seq data are over-dispersed counts, and so analysis methods must also account for the non-normality of the data. In this study we evaluate the utility of several common statistical methods for correlated, over-dispersed count data in the context of RNA-Seq experiments via a simulation study and application to a longitudinal RNA-Seq dataset. The methods compared include generalized estimating equations, generalized linear mixed models, and linear mixed models after taking a normalizing transformation of the count data. We also compare these methods to popular approaches for analyzing RNA-Seq data using the edgeR, DESeq2 and limma packages in R. Additionally, for each method we explore the use of several degrees of freedom approximations used in significance testing. Finally, recommendations as to which methods are most appropriate under various circumstances are provided.

Statistical Methods for Rates and Proportions

Statistical Methods for Rates and Proportions
A Book

by Joseph L. Fleiss,Bruce Levin,Myunghee Cho Paik

  • Publisher : John Wiley & Sons
  • Release : 2013-06-12
  • Pages : 800
  • ISBN : 1118625617
  • Language : En, Es, Fr & De
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* Includes a new chapter on logistic regression. * Discusses the design and analysis of random trials. * Explores the latest applications of sample size tables. * Contains a new section on binomial distribution.

Model Based Inference in the Life Sciences

Model Based Inference in the Life Sciences
A Primer on Evidence

by David R. Anderson

  • Publisher : Springer Science & Business Media
  • Release : 2007-12-22
  • Pages : 184
  • ISBN : 0387740759
  • Language : En, Es, Fr & De
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This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.

Applied Categorical and Count Data Analysis

Applied Categorical and Count Data Analysis
A Book

by Wan Tang,Hua He,Xin M. Tu

  • Publisher : CRC Press
  • Release : 2012-06-04
  • Pages : 384
  • ISBN : 1439806241
  • Language : En, Es, Fr & De
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Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments. The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.

Handbook of Statistical Methods for Randomized Controlled Trials

Handbook of Statistical Methods for Randomized Controlled Trials
A Book

by KyungMann Kim,Frank Bretz,Ying Kuen K. Cheung,Lisa V. Hampson

  • Publisher : CRC Press
  • Release : 2021-08-23
  • Pages : 654
  • ISBN : 1498714641
  • Language : En, Es, Fr & De
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Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.

Statistical Methods in Toxicology

Statistical Methods in Toxicology
Proceedings of a Workshop during EUROTOX ’90 Leipzig, Germany, September 12–14, 1990

by Ludwig Hothorn

  • Publisher : Springer Science & Business Media
  • Release : 2013-03-08
  • Pages : 161
  • ISBN : 364248736X
  • Language : En, Es, Fr & De
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This book contains selected papers from a workshop on modern statistical methods in toxicology held during the EUROTOX '90 conference in Leipzig. The papers deal with the biostatistical evaluation of the commonly used toxicological assays, i.e. mutagenicity, long-term carcinogenicity, embryotoxicity and chronic toxicity assays. The biological background is considered in detail, and most of the related statistical approaches described. In five overview papers, the present state of the art of the related topics is given, while in several contributed papers new approaches are discussed. The most important features are: - A new view on the per-litter analysis problem in em- bryotoxicity assays. - A highly sophisticated treatment of the so-called muta-tox problem in mutagenicity assays. - A detailed discussion of the multiplicity problem based on the closed testing procedure. This volume provides readers with an overview of modern biostatistical methods for several toxicological assays and is in part intended for direct, practical use.

Econometric Analysis of Count Data

Econometric Analysis of Count Data
A Book

by Rainer Winkelmann

  • Publisher : Springer Science & Business Media
  • Release : 2013-11-11
  • Pages : 304
  • ISBN : 3540247289
  • Language : En, Es, Fr & De
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Graduate students and researchers are provided with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally, applications are reviewed in fields such as economics, marketing, sociology, demography, and health sciences. The fourth edition contains several new sections, for example on nonnested hurdle models, quantile regression and on software. Many other sections have been entirely rewritten and extended.

Statistical Methods for Time-conditional Survival Probability and Equally Spaced Count Data

Statistical Methods for Time-conditional Survival Probability and Equally Spaced Count Data
A Book

by Victoria A. Gamerman

  • Publisher : Unknown Publisher
  • Release : 2016
  • Pages : 354
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This dissertation develops statistical methods for time-conditional survival probability and for equally spaced count data. Time-conditional survival probabilities are an alternative measure of future survival by accounting for time elapsed from diagnosis and are estimated as a ratio of survival probabilities. In Chapter 2, we derive the asymptotic distribution of a vector of nonparametric estimators and use weighted least squares methodology for the analysis of time-conditional survival probabilities. We show that the proposed test statistics for evaluating the relationship between time-conditional survival probabilities and additional time survived have central Chi-Square distributions under the null hypotheses. Further, we conducted simulation studies to assess the empirical probability of making a type I error for one of the hypotheses tests developed and to assess the power of the various models and statistics proposed. Additionally, we used weighted least squares techniques to fit regression models for the log time-conditional survival probabilities as a function of time survived after diagnosis to address clinically relevant questions. In Chapter 3, we derive the asymptotic distribution of time-conditional survival probability estimators from a Weibull parametric regression model and from a Logistic-Weibull cure model, adjusting for continuous covariates. We implement the weighted least squares methodology to assess relevant hypotheses. We create a statistical framework for investigating time-conditional survival probability by developing additional methodological approaches to address the relationship between estimated time-conditional survival probabilities, time survived, and patient prognostic factors. Over-dispersed count data are often encountered in longitudinal studies. In Chapter 4, we implement a maximum-likelihood based method for the analysis of equally spaced longitudinal count data with over-dispersion. The key features of this approach are first-order antedependence and linearity of the conditional expectations. We also assume a Markovian model of first order, implying that the value of an outcome on a subject at a specific measurement occasion only depends on the value at the previous measurement occasion. Our maximum likelihood approach using the Poisson model for count data benefits from a simple interpretation of regression parameters, like that in GEE analysis of count data.

Analysis of Longitudinal Data with Example

Analysis of Longitudinal Data with Example
A Book

by You-Gan Wang,Liya Fu,Sudhir Paul

  • Publisher : CRC Press
  • Release : 2022-01-28
  • Pages : 248
  • ISBN : 1498764622
  • Language : En, Es, Fr & De
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Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code

Methods and Applications of Statistics in Clinical Trials, Volume 2

Methods and Applications of Statistics in Clinical Trials, Volume 2
Planning, Analysis, and Inferential Methods

by N. Balakrishnan

  • Publisher : John Wiley & Sons
  • Release : 2014-06-16
  • Pages : 960
  • ISBN : 1118595963
  • Language : En, Es, Fr & De
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Methods and Applications of Statistics in Clinical Trials,Volume 2: Planning, Analysis, and Inferential Methods includesupdates of established literature from the Wiley Encyclopedia ofClinical Trials as well as original material based on the latestdevelopments in clinical trials. Prepared by a leading expert, thesecond volume includes numerous contributions from currentprominent experts in the field of medical research. In addition,the volume features: • Multiple new articles exploring emerging topics, such asevaluation methods with threshold, empirical likelihood methods,nonparametric ROC analysis, over- and under-dispersed models, andmulti-armed bandit problems • Up-to-date research on the Cox proportional hazardmodel, frailty models, trial reports, intrarater reliability,conditional power, and the kappa index • Key qualitative issues including cost-effectivenessanalysis, publication bias, and regulatory issues, which arecrucial to the planning and data management of clinical trials

Statistical Analysis of Microbiome Data with R

Statistical Analysis of Microbiome Data with R
A Book

by Yinglin Xia,Jun Sun,Ding-Geng Chen

  • Publisher : Springer
  • Release : 2018-10-06
  • Pages : 505
  • ISBN : 9811315345
  • Language : En, Es, Fr & De
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This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.

Statistical Analysis of Microbiome Data

Statistical Analysis of Microbiome Data
A Book

by Somnath Datta

  • Publisher : Springer Nature
  • Release : 2022
  • Pages : 129
  • ISBN : 3030733513
  • Language : En, Es, Fr & De
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Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics
A Book

by J. Philip Miller

  • Publisher : Elsevier
  • Release : 2010-11-08
  • Pages : 368
  • ISBN : 9780444537386
  • Language : En, Es, Fr & De
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Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

Introduction to Statistical Methods for Biosurveillance

Introduction to Statistical Methods for Biosurveillance
With an Emphasis on Syndromic Surveillance

by Ronald D. Fricker

  • Publisher : Cambridge University Press
  • Release : 2013-02-25
  • Pages : 129
  • ISBN : 1107328063
  • Language : En, Es, Fr & De
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Bioterrorism is not a new threat, but in an increasingly interconnected world, the potential for catastrophic outcomes is greater today than ever. The medical and public health communities are establishing biosurveillance systems designed to proactively monitor populations for possible disease outbreaks as a first line of defense. The ideal biosurveillance system should identify trends not visible to individual physicians and clinicians in near-real time. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation, quantification, localization and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical training. Weaving public health and statistics together, it presents basic and more advanced methods, with a focus on empirically demonstrating added value. Although the emphasis is on epidemiologic and syndromic surveillance, the statistical methods can be applied to a broad class of public health surveillance problems.

Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases

Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases
A Book

by Katherine E. Irimata,Brittany N. Dugger,Jeffrey R. Wilson

  • Publisher : Johns Hopkins University Press
  • Release : 2020-05-05
  • Pages : 480
  • ISBN : 142143671X
  • Language : En, Es, Fr & De
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Allowing more people to aid in analyzing data—while promoting constructive dialogues with statisticians—this book will hopefully play an important part in unlocking the secrets of these confounding diseases.

Cancer Clinical Trials

Cancer Clinical Trials
Current and Controversial Issues in Design and Analysis

by Stephen L. George,Xiaofei Wang,Herbert Pang

  • Publisher : CRC Press
  • Release : 2016-08-03
  • Pages : 474
  • ISBN : 1315354330
  • Language : En, Es, Fr & De
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Cancer Clinical Trials: Current and Controversial Issues in Design and Analysis provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. Well-known statisticians from academic institutions, regulatory and government agencies (such as the U.S. FDA and National Cancer Institute), and the pharmaceutical industry share their extensive experiences in cancer clinical trials and present examples taken from actual trials. The book covers topics that are often perplexing and sometimes controversial in cancer clinical trials. Most of the issues addressed are also important for clinical trials in other settings. After discussing general topics, the book focuses on aspects of early and late phase clinical trials. It also explores personalized medicine, including biomarker-based clinical trials, adaptive clinical trial designs, and dynamic treatment regimes.

Statistical Data Analysis Based on the L1-Norm and Related Methods

Statistical Data Analysis Based on the L1-Norm and Related Methods
A Book

by Yadolah Dodge

  • Publisher : Birkhäuser
  • Release : 2012-12-06
  • Pages : 456
  • ISBN : 3034882017
  • Language : En, Es, Fr & De
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This volume contains a selection of invited papers, presented to the fourth International Conference on Statistical Data Analysis Based on the L1-Norm and Related Methods, held in Neuchâtel, Switzerland, from August 4–9, 2002. The contributions represent clear evidence to the importance of the development of theory, methods and applications related to the statistical data analysis based on the L1-norm.

Safety and Security in Transit Environments

Safety and Security in Transit Environments
An Interdisciplinary Approach

by Vania Ceccato,Andrew Newton

  • Publisher : Springer
  • Release : 2015-06-30
  • Pages : 390
  • ISBN : 1137457651
  • Language : En, Es, Fr & De
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Safety and Security in Transit Environments presents interdisciplinary studies from leading international authors. This important volume identifies key challenges and complexities in addressing security and safety concerns in transit settings, policy recommendations for prevention, and new frontiers for research at transit settings. Chapter 9 of this book is open access under a CC BY license via link.springer.com.