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  • Introduction /M. Alma Rodriguez --History of MD Anderson's tumor registry /Sarah H. Taylor --Statistical methods /Geoffrey G. Giacco, Sarah H. Taylor and Kenneth R. Hess --Breast cancer /Aman U. Buzdar ... [et al.] --Prostate cancer /Deborah A. Kuban ... [et al.] --Non-small cell lung cancer /Ritsuko Komaki, Anne S. Tsao, and Reza J. Mehran --Small cell lung cancer /Frank V. Fossella --Colon cancer /Cathy Eng, Patrick Lynch, and John Skibber --Ovarian cancer /Robert L. Coleman and David M. Gershenson --Cervical cancer /Patricia J. Eifel and Charles Levenback --Endometrial cancer /Thomas Burke ... [et al.] --Pancreatic cancer (exocrine) /Jason Fleming ... [et al.] --Kidney cancer /Scott E. Delacroix Jr. ... [et al.] --Bladder cancer /Robert S. Svatek ... [et al.] --Cutaneous melanoma /Jeffrey E. Gershenwald, Geoffrey G. Giacco, and Jeffrey E. Lee --Liver cancer /Evan S. Glazer and Steven A. Curley --Esophageal cancer /Linus Ho ... [et al.] --Gastric cancer /Alexandria T. Phan and Paul F. Mansfield --Acute myeloid leukemia /Emil J. Freireich --Chronic lymphocytic leukemia/small lymphocytic lymphoma /Apostolia-Maria Tsimberidou and Michael J. Keating --Hodgkin lymphoma /Michelle Fanale ... [et al.] --Non-hodgkin indolent B-cell lymphoma /Sattva S. Neelapu --Non-hodgkin aggressive B-cell lymphoma /M. Alma Rodriguez --Multiple myeloma /Donna Weber and Raymond Alexanian --Head and neck cancer /Ehab Hanna ... [et al.] --Thyroid cancer /Steven I. Sherman, Nancy Perrier, and Gary L. Clayman --Soft tissue sarcomas /Vinod Ravi, Raphael Pollock, and Shreyaskumar R. Patel --Sarcomas of bone /Valerae Lewis.
  • "Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers' projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge -- Explains the related background on hardware, architecture and programming for ease of use -- Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects."--Provided by publisher.
  • Protocol amendment -- Adaptive randomization -- Adaptive hypotheses -- Adaptive dose-escalation trials -- Adaptive group sequential design -- Statistical tests for adaptive seamless designs -- Adaptive sample size adjustment -- Two-stage adaptive design -- Adaptive treatment switching -- Bayesian approach -- Biomarker adaptive trials -- Target clinical trials -- Sample size and power estimation -- Clinical trial simulation -- Regulatory perspectives : a review of FDA draft guidance.
  • 1. Introduction -- 2. Medical tests and preliminary information -- 3. Preview of the book -- 4. Fundamentals of diagnostic accuracy -- 5. Regression and medical test accuracy -- 6. Agreement and test accuracy -- 7. Estimating test accuracy with an imperfect reference standard -- 8. Verification bias and test accuracy -- 9. Test accuracy and medical practice -- 10. Accuracy of combined tests -- 11. Bayesian methods of meta-analysis.
  • Analysis of clinical trials using SAS 2005, books24x7, SUNet ID login required.
    Fulltext ProQuest Safari
    Analysis of Stratified Data -- Multiple Comparisons and Multiple Endpoints -- Analysis of Safety and Diagnostic Data -- Interim Data Monitoring -- Analysis of Incomplete Data.
  • Food intake and energy metabolism -- Glucose homeostasis -- Optimal glucose homeostasis -- Bistability as a fundamental phenomenon -- Biomolecular network -- P13K-AKT-TOR pathway -- Diseases related to metabolism -- Mathematical modeling of the P13K-AKT-TOR Pathway -- Fundamental decomposition -- Normal phenotype -- Disease phenotypes -- Tao of diseases.
  • "Adding topics useful to medical statisticians, this new edition of a popular intermediate-level reference explores the use of SAS for analyzing medical data. A new chapter on visualizing data includes a detailed account of graphics for investigating data and smoothing techniques. The book also includes new chapters on measurement in medicine, epidemiology/observational studies, meta-analysis, Bayesian methods, and handling missing data. The book maintains its example-based approach, with SAS code and output included throughout and available online"--Provided by publisher.
  • "Preface In Chapter 8 of our previous book (Chen and Peace, 2010), we briefy introduced meta-analysis using R. Since then, we have been encouraged to develop an entire book on meta-analyses using R that would include a wide variety of applications - which is the theme of this book. In this book we provide a thorough presentation of meta-analysis with detailed step-by-step illustrations on their implementation using R. In each chapter, examples of real studies compiled from the literature and scienti c publications are presented. After presenting the data and sufficient background to permit understanding the application, various meta-analysis methods appropriate for analyzing data are identi ed. Then analysis code is developed using appropriate R packages and functions to meta-analyze the data. Analysis code development and results are presented in a stepwise fashion. This stepwise approach should enable readers to follow the logic and gain an understanding of the analysis methods and the R implementation so that they may use R and the steps in this book to analyze their own meta-data. Based on their experience in biostatistical research and teaching biostatistical meta-analysis, the authors understand that there are gaps between developed statistical methods and applications of statistical methods by students and practitioners. This book is intended to ll this gap by illustrating the implementation of statistical mata-analysis methods using R applied to real data following a step-by-step presentation style. With this style, the book is suitable as a text for a course in meta-data analysis at the graduate level (Master's or Doctorate's), particularly for students seeking degrees in statistics or biostatistics"--Provided by publisher.
  • Chapter 1. Statistical approaches for clinical trials -- Chapter 2. Basics of Bayesian inference -- Chapter 3. Phase I studies -- Chapter 4. Phase II studies -- Chapter 5. Phase III studies -- Chapter 6. Special topics.
  • "Although the popularity of the Bayesian approach to statistics has been growing for years, many still think of it as somewhat esoteric, not focused on practical issues, or generally too difficult to understand. Bayesian Analysis Made Simple is aimed at those who wish to apply Bayesian methods but either are not experts or do not have the time to create WinBUGS code and ancillary files for every analysis they undertake. Accessible to even those who would not routinely use Excel, this book provides a custom-made Excel GUI, immediately useful to those users who want to be able to quickly apply Bayesian methods without being distracted by computing or mathematical issues.From simple NLMs to complex GLMMs and beyond, Bayesian Analysis Made Simple describes how to use Excel for a vast range of Bayesian models in an intuitive manner accessible to the statistically savvy user. Packed with relevant case studies, this book is for any data analyst wishing to apply Bayesian methods to analyze their data, from professional statisticians to statistically aware scientists"--Provided by publisher.
  • "Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples."--Jacket.
  • Bayesian inference and modeling -- Computational issues -- Residuals and goodness-of-fit -- Disease map reconstruction and relative risk estimation -- Disease cluster detection -- Regression and ecological analysis -- Putative hazard modeling -- Multiple scale analysis -- Multivariate disease analysis -- Spatial survival and longitudinal analysis -- Spatiotemporal disease mapping -- Disease map surveillance.
  • Written by a biostatistics expert with over 20 years of experience in the field, Bayesian Methods in Epidemiology presents statistical methods used in epidemiology from a Bayesian viewpoint. It employs the software package WinBUGS to carry out the analyses and offers the code in the text and for download online. The book examines study designs that investigate the association between exposure to risk factors and the occurrence of disease. It covers introductory adjustment techniques to compare mortality between states and regression methods to study the association between various risk factors.
  • "Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties. Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria."--Publisher's description.
  • Chapter 1. Estimation and Testing in Time-Course Microarray Experiments -- Chapter 2. Classification for Differential Gene Expression Using Bayesian Hierarchical Models -- Chapter 3. Applications of MOSS for Discrete Multi-Way Data -- Chapter 4. Nonparametric Bayesian Bioinformatics -- Chapter 5. Measurement Error and Survival Model for cDNA Microarrays -- Chapter 6. Bayesian Robust Inference for Differential Gene Expression -- Chapter 7. Bayesian Hidden Markov Modeling of Array CGH Data -- Chapter 8. Bayesian Approaches to Phylogenetic Analysis -- Chapter 9. Gene Selection for the Identification of Biomarkers in High-Throughput Data -- Chapter 10. Sparsity Priors for Protein - Protein Interaction Predictions -- Chapter 11. Learning Bayesian Networks for Gene Expression Data -- Chapter 12. In-Vitro to In-Vivo Factor Profiling in Expression Genomics -- Chapter 13. In-Vitro to In-Vivo Factor Profiling in Expression Genomics Machines -- Chapter 14. A Bayesian Mixture Model for Protein Biomarker Discovery -- Chapter 15. Bayesian Methods for Detecting Differentially Expressed Genes -- Chapter 16. Bayes and Empirical Bayes Methods for Spotted Microarray Data Analysis -- Chapter 17. Bayesian Classification Method for QTL Mapping.
  • Biomedical Signals and Systems is meant to accompany a one-semester undergraduate signals and systems course. It may also serve as a quick-start for graduate students or faculty interested in how signals and systems techniques can be applied to living systems. The biological nature of the examples allows for systems thinking to be applied to electrical, mechanical, fluid, chemical, thermal and even optical systems. Each chapter focuses on a topic from classic signals and systems theory: System block diagrams, mathematical models, transforms, stability, feedback, system response, control, time and frequency analysis and filters. Embedded within each chapter are examples from the biological world, ranging from medical devices to cell and molecular biology. While the focus of the book is on the theory of analog signals and systems, many chapters also introduce the corresponding topics in the digital realm. Although some derivations appear, the focus is on the concepts and how to apply them. Throughout the text, systems vocabulary is introduced which will allow the reader to read more advanced literature and communicate with scientist and engineers. Homework and Matlab simulation exercises are presented at the end of each chapter and challenge readers to not only perform calculations and simulations but also to recognize the real-world signals and systems around them.
  • Chapter 1. Scientific notation and metric prefixes -- Chapter 2. Solutions, mixtures, and media -- Chapter 3. Cell growth -- Chapter 4. Working with bacteriophages -- Chapter 5. Nucleic acid quantification -- Chapter 6. Labeling nucleic acids with radioisotopes -- Chapter 7. Oligonucleotide synthesis -- Chapter 8. The polymerase chain reaction (PCR) -- Chapter 9. The real-time polymerase chain reaction (RT-PCR) -- Chapter 10. Recombinant DNA -- Chapter 11. Protein -- Chapter 12. Centrifugation -- Chapter 13. Forensics and paternity.
  • Cellular potts models 2013, CRCnetBASE
    "All biological phenomena emerge from an intricate interconnection of multiple processes occurring at different levels of organization: namely, at the molecular, the cellular and the tissue level, see Figure 1. These natural levels can approximately be connected to a microscopic, mesoscopic, and macroscopic scale, respectively. The microscopic scale refers to those processes that occur at the subcellular level, such as DNA synthesis and duplication, gene dynamics, activation of receptors, transduction of chemical signals, diffusion of ions and transport of proteins. The mesoscopic scale, on the other hand, can refer to cell-level phenomena, such as adhesive interactions between cells or between cells and ECM components, cell duplication and death and cell motion. The macroscopic scale finally corresponds to those processes that are typical of multicellular behavior, such as population dynamics, tissue mechanics and organ growth and development. It is evident that research in biology and medicine needs to work in a multiscale fashion. This brings many challenging questions and a complexity that can not be addressed in the classical way, but can take advantage of the increasing collaboration between natural and exact sciences (for more detailed comments the reader is referred to [90, 262]). On the other hand, the recent literature provides evidence of the increasing attention of the mathematical, statistical, computational and physical communities toward biological and biomedical modeling, consequence of the successful results obtained by a multidisciplinary approach to the Life Sciences problems"-- Provided by publisher.
  • "With examples based on the authors' 30 years of real-world experience in many areas of clinical drug development, this book provides a thorough presentation of clinical trial methodology. It presents detailed step-by-step illustrations on the implementation of the open-source software R. Case studies demonstrate how to select the appropriate clinical trial data. The authors introduce the corresponding biostatistical analysis methods, followed by the step-by-step data analysis using R. They also offer the R program for download, along with other essential data, on their website"--Provided by publisher.
  • Cluster randomised trials 2009, CRCnetBASE
    Variability between clusters -- Choosing whether to randomise by cluster -- Choice of clusters -- Matching and stratification -- Randomisation procedures -- Sample size -- Alternative study designs -- Basic principles of analysis -- Analysis based on cluster-level summaries -- Regression analysis based on individual level data -- Analysis of trials with more complex designs -- Ethnical considerations -- Data monitoring -- Reporting and interpretation.
  • Introduction & basics -- Topics in hypothesis testing -- The data set TRIAL -- The one-sample t-test -- The two-sample t-test -- One-way ANOVA -- Two-way ANOVA -- Repeated measures analysis -- The crossover design -- Linear regression -- Analysis of covariance -- The Wilcoxon signed-rank test -- The Wilcoxon rank-sum test -- The Kruskal-Wallis test -- The binomial test -- The Chi-square test -- Fisher's exact test -- McNemar's test -- The Cochran-Mantel-Haenszel test -- Logistic regression -- The log-rank test -- The Cox proportional hazards model -- Exercises.
  • Recent trends in health care across the United States and internationally have emphasized a novel approach that consists in comparing the effectiveness and efficacy of treatment interventions with a patient-centered emphasis (i.e., evidence-based health care), while ensuring cost constraints, maximizing benefits, and minimizing risks. In this book, experts in comparative effectiveness and efficacy research and analysis for practice (CEERAP) in health care in general address a range of topical issues. The emphasis is on implications for endodontics and nursing, both of which are considered in a series of detailed chapters. Commonalities and differences among CEERAP, utility-based and logic-based analysis and decision-making, and evidence-based and patient-centered practice are defined and discussed. The book concludes by examining applications for CEERAP in developing patient-centered optimal treatment interventions for the next decade.
  • Data manipulation with R 2008, Springer
  • Data mining and knowledge discovery handbook 2005, books24x7, SUNet ID login required.
  • Data mining with R 2011, CRCnetBASE
    "This hands-on book uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, it covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. The main data mining processes and techniques are presented through detailed, real-world case studies. With these case studies, the author supplies all necessary steps, code, and data. Mirroring the do-it-yourself approach of the text, the supporting website provides data sets and R code" -- Provided by publisher.
  • Part 1. Explorations -- Introduction -- Getting Started -- Working with Data -- Loading Data -- Exploring Data -- Interactive Graphics -- Transforming Data -- Part 2. Building Models -- Descriptive and Predictive Analytics -- Cluster Analysis -- Association Analysis -- Decision Trees -- Random Forests -- Boosting -- Support Vector Machines -- Part 3. Delivering Performance -- Model Performance Evaluation -- Deployment -- Part 4. Appendices -- Installing Rattle -- Sample Datasets.
  • Data preparation for analytics using SAS 2006, books24x7, SUNet ID login required.
    Pt. 1. Data preparation: business point of view -- ch. 1. Analytic business questions -- Ch. 2. Characteristics of analytic business questions -- Ch. 3. Characteristics of data sources -- Ch. 4. Different points of view on analytic data preparation -- Pt. 2. Data structures and data modeling -- Ch. 5. The origin of data -- Ch. 6. Data models -- Ch. 7. Analysis subjects and multiple observations -- Ch. 8. The one row-per-subject data mart -- Ch. 9. The multiple-rows-per-subject data mart -- Ch. 10. Data structures for longitudinal analysis -- Ch. 11. Considerations for data marts -- Ch. 11. Considerations for predictive modeling -- Pt. 3. Data mart coding and content -- Ch. 13. Accessing data -- Ch. 14. Transposing one- and multiple-rows-per-subject data structures -- Ch. 15. Transposing longitudinal data -- Ch. 16. Transformations of interval-scaled variables -- Ch. 17. Transformations of categorical variables -- Ch. 18. Multiple interval-scaled observations per subject -- Ch. 19. Multiple catagorical observations per subject -- Ch. 20. Coding for predictive modeling -- Ch. 21. Data preparation for multiple-rows-per-subject and longitudinal data marts -- Pt. 4. Sampling, scoring, and automation -- Ch. 22. Sampling -- Ch. 23. Scoring and automation -- Ch 24. Do's and don'ts when building data marts -- Pt. 5. Case studies.
  • Overview of time-to-event endpoint methodology / Karl E. Peace -- Design (and monitoring) of clinical trials with time-to-event endpoints / Michael W. Sill and Larry Rubinstein -- Overview of time-to-event parametric methods / Karl E. Peace and Kao-Tai Tsai -- Overview of semiparametric inferential methods for time-to-event endpoints / Jianween Cai and Donglin Zeng -- Overview of inferential methods for categorical time-to-event data / Eric V. Slud -- Overview of Bayesian inferential methods including time-to-event endpoints / Laura H. Gunn -- An efficient alternative to the Cox model for small time-to-event trials / Devan V. Mehrotra and Arthur J. Roth -- Estimation and testing for change in hazard for time-to-event endpoints / Rafia Bhore and Mohammad Huque -- Overview of descriptive and graphical methods for time-to-event data / Michael O'Connell and Bob Treder -- Design and analysis of analgesic trials / Akiko Okamoto, Julia Wang, and Surya Mohanty -- Design and analysis of analgesic trials with paired time-to-event endpoints / Zhu Wang and Hon Keung Tony Ng --Time-to-event endpoint methods in antibiotic trials / Karl E. Peace -- Design and analysis of cardiovascular prevention trials / Michelle McNabb and Andreas Sashegyi -- Design and analysis of antiviral trials / Anthony C. Segreti and Lynn P. Dix -- Cure rate models with applications to melanoma and prostate cancer data / Ming-Hui Chen and Sungduk Kim -- Parametric likelihoods for multiple nonfatal competing risks and death, with application to cancer data / Peter F. Thall and Xuemei Wang -- Design, summarization, analysis, and interpretation of cancer prevention trials / Matthew C. Somerville, Jennifer B. Shannon, and Timothy H. Wilson -- LASSO method in variable selection for right-censored time-to-event data with application to astrocytoma brain tumor and chronic myelogonous leukemia / Lili Yu and Dennis Pearl -- Selecting optimal treatments based on predictive factors / Eric C. Polley and Mark J. van der Laan -- Application of time-to-event methods in the assessment of safety in clinical trials / Kelly L. Moore and Mark J. van der Laan -- Design and analysis of chronic carcinogenicity studies of pharmaceuticals in rodents / Mohammad Atiar Rahman and Karl K. Lin -- Design and analysis of time-to-tumor response in animal studies : a Bayesian perspective / Steve Thomson and Karl K. Lin.
  • 1. Introduction -- 2. Diagnostic checks for univariate linear models -- 3. The multivariate linear case -- 4. Robust modeling and diagnostic checking -- 5. Nonlinear models -- 6. Conditional heteroscedasticity models -- 7. Fractionally differenced process -- 8. Miscellaneous models and topics.
  • Changing patterns of disease and mortality in Sub-Saharan Africa: an overview /Florence K. Baingana and Eduard R. Bos --Levels and trends in mortality in Sub-Saharan Africa: an overview /Jacob Adetunji and Eduard R. Bos --Trends in child mortality, 1960 to 2000 /Kenneth Hill and Agbessi Amouzou --Levels and trends of adult mortality /Debbie Bradshaw and Ian M. Timaeus --Causes of death /Chalapati Rao, Alan D. Lopez, and Yusuf Hemed --Population and mortality after AIDS /Rodolfo A. Bulatao --Levels and patterns of mortality at INDEPTH demographic surveillance systems /Osman A. Sankoh ... [et al.] --Trends and issues in child undernutrition /Todd Benson and Meera Shekar --Diarrheal diseases /Cynthia Boschi-Pinto, Claudio F. Lanata, Walter Mendoza, and Demissie Habte --Developmental disabilities /Geoff Solarsh and Karen J. Hofman --Acute respiratory infections /Shabir A. Mahdi and Keith P. Klugman --Vaccine-preventable diseases /Mark A. Miller and John T. Sentz --Tuberculosis /Christopher Dye ... [et al.] --Malaria /Robert W. Snow and Judy A. Omumbo --Onchocerciasis /Uche Amazigo ... [et al.] --Maternal mortality /Khama O. Rogo, John Oucho, and Philip Mwalali --HIV/AIDS /Souleymane Mboup ... [et al.] --Lifestyle and related risk factors for chronic diseases /Krisela Steyn and Albertino Damasceno --Diabetes mellitus /Jean-Claude Mbanya and Kaushik Ramiaya --Cancers /Freddy Sitas ... [et al.] --Cardiovascular disease /Anthony Mbewu and Jean-Claude Mbanya --Mental health and the abuse of alcohol and controlled substances /Florence K. Baingana, Atalay Alem, and Rachel Jenkins --Neurological disorders /Donald Silberberg and Elly Katabira --Violence and injuries /Brett Bowman ... [et al.].
  • Doing Bayesian data analysis 2011, ProQuest Safari
    This book's organization : read me first! -- Introduction : models we believe in -- What is this stuff called probability? -- Bayes' rule -- Inferring a binomial proportion via exact mathematical analysis -- Inferring a binomial proportion via grid approximation -- Inferring a binomial proportion via the Metropolis algorithm -- Inferring two binomial proportions via Gibbs sampling -- Bernoulli likelihood with hierarchical prior -- Hierarchical modeling and model comparison -- Null hypothesis significance testing -- Bayesian approaches to testing a point ("null") hypothesis -- Goals, power, and sample size -- Overview of the generalized linear model -- Metric predicted variable on a single group -- Metric predicted variable with one metric predictor -- Metric predicted variable with multiple metric predictors -- Metric predicted variable with one nominal predictor -- Metric predicted variable with multiple nominal predictors -- Dichotomous predicted variable -- Ordinal predicted variable -- Contingency table analysis -- Tools in the trunk.
  • Part I. Fundamentals -- Chapter 1. Introduction -- Chapter 2. Dose Finding in Clinical Trials -- Chapter 3. The Continual Reassessment Method -- Chapter 4. One-Parameter Dose-Toxicity Models -- Chapter 5. Theoretical Properties -- Chapter 6. Empirical Properties -- Part II. Design Calibration -- Chapter 7. Specifications of a CRM Design -- Chapter 8. Initial Guesses of Toxicity Probabilities -- Chapter 9. Least Informative Normal Prior -- Chapter 10. Initial Design -- Part III. CRM and Beyond -- Chapter 11. The Time-to-Event CRM -- Chapter 12. CRM with Multiparameter Models -- Chapter 13. When the CRM Fails -- Chapter 14. Stochastic Approximation.
  • DSM-IV-TR 2000, PsychiatryOnline
  • "In the last twenty years, dynamic prediction models have been extensively used to monitor patient prognosis in survival analysis. Written by one of the pioneers in the area, this book synthesizes these developments in a unified framework. It covers a range of models, including prognostic and dynamic prediction of survival using genomic data and time-dependent information. The text includes numerous examples using real data that is taken from the authors collaborative research. R programs are provided for implementing the methods"--Provided by publisher.
  • Empirical likelihood 2001, CRCnetBASE
  • Reference tool covering statistics, probability theory, biostatistics, quality control, and economics with emphasis in applications of statistical methods in sociology, engineering, computer science, biomedicine, psychology, survey methodology, and other client disciplines.
  • EQ-5D value sets 2007, Springer
  • Historical Introduction --Descriptive Biostatistics --Inferential Biostatistics (I): Estimating Values of Biomedical Magnitudes --Inferential Biostatistics (II): Estimating Biomedical Behaviors --Equations: Formulating Biomedical Laws and Biomedical Magnitudes --Systems of Equations: The Explanation of Biomedical Phenomena (I). Basic Questions --Systems of Equations: The Explanation of Biomedical Phenomena (II). Dynamic Interdependencies --Optimal Control Theory: From Knowledge to Control (I). Basic Concepts --Optimal Control Theory: From Knowledge to Control (II). Biomedical Applications --Game Theory.
  • Excel hacks. 2nd ed. 2007, ProQuest Safari
    Reducing workbook and worksheet frustration -- Hacking Excel's built-in features -- Naming hacks -- Hacking PivotTables -- Hacking formulas and functions -- Macro hacks -- Cross-application hacks.
  • Frailty model 2008, Springer
  • The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.--From the publisher's website.
  • Ggplot2 2009, Springer
    Describes ggplot2, a data visualization package for R and a powerful and flexible system for creating data graphics.
  • Google Analytics 2.0 2007, books24x7
  • ch. 1. Mathematical results on scale-free random graphs / Béla Bollobás, Oliver M. Riordan -- ch. 2. Random graphs as models of networks / Mark E. J. Newman -- ch. 3. Emergence of scaling in complex networks / Albert-László Barabási -- ch. 4. Structural properties of scale-free networks / Reuven Cohen, Shlomo Havlin, Daniel ben-Avraham -- ch. 5. Epidemics and immunization in scale-free networks / Romualdo Pastor-Satorras, Alessandro Vespignani -- ch. 6. Cells and genes as networks in nematode development and evolution / Ralf J. Sommer -- ch. 7. Complex networks in genomics and proteomics / Ricard V. Solé, Romualdo Pastor-Satorras -- ch. 8. Correlation profiles and motifs in complex networks / Sergei Maslov, Kim Sneppen, Uri Alon -- ch. 9. Theory of interacting neural networks / Wolfgang Kinzel -- ch. 10. Modelling food webs / Barbara Drossel, Alan J. McKane -- ch. 11. Traffic networks / Kai Nagel -- ch. 12. Economic networks / Alan Kirman -- ch. 13. Local search in unstructured networks / Lada A. Adamic, Rajan M. Lukose, Bernardo A. Huberman -- ch. 14. Accelerated growth of networks / Sergei N. Dorogovtsev, Jose F. F. Mendes -- ch. 15. Social percolators and self organized criticality / Gérard Weisbuch, Sorin Solomon -- ch. 16. Graph theory and the evolution of autocatalytic networks / Sanjay Jain, Sandeep Krishna.
  • Bayesian multilevel analysis and MCMC /David Draper --Diagnostic checks for multilevel models /Tom A.B. Snijders, Johannes Berkhof --Optimal designs for multilevel studies /Mirjam Moerbeek, Gerard J.P. Van Breukelen, Martijn P.F. Berger --Many small groups /Stephen W. Raudenbush --Multilevel models for ordinal and nominal variables /Donald Hedeker --Multilevel and related models for longitudinal data /Anders Skrondal, Sophia Rabe-Hesketh --Non-hierarchical multilevel models /Jon Rasbash, William J. Browne --Multilevel generalized linear models /Germán Rodríguez --Missing data /Nicholas T. Longford --Resampling multilevel models /Rien van der Leeden, Erik Meijer, Frank M.T.A. Busing --Multilevel structural equation modeling /Stephen H.C. du Toit, Mathilda du Toit.
  • An introduction to R -- Data analysis using graphical displays -- Simple inference -- Conditional inference -- Analysis of variance -- Simple and multiple linear regression -- Logistic repression and generalised linear models -- Density estimation -- Recursive partitioning -- Smoothers and generalised additive models -- Survival analysis -- Analysing longitudinal data I -- Analysing longitudinal data II -- Simultaneous inference and multiple comparisons -- Meta-analysis -- Principal component analysis -- Multidimensional scaling -- Cluster analysis.
  • Pt. 1. Phase I trials -- pt. 2. Phase II trials -- pt. 3. Phase III trials -- pt. 4. Exploratory and high -dimensional data analyses.
  • "Handbook of survival analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides an introduction to various areas in survival analysis for graduate students and novices, a reference to modern investigations into survival analysis for more established researchers, a text or supplement for a second or advanced course in survival analysis, and a useful guide to statistical methods for analyzing survival data experiments for practicing statisticians"--Publisher's description.
  • Chapter 1. Toward Healthcare Improvement Using Analytics -- Chapter 2. Fundamentals of Healthcare Analytics -- Chapter 3. Developing an Analytics Strategy to Drive Change -- Chapter 4. Defining Healthcare Quality and Value -- Chapter 5. Data Quality and Governance -- Chapter 6. Working with Data -- Chapter 7. Developing and Using Effective Indicators -- Chapter 8. Leveraging Analytics in Quality Improvement Activities -- Chapter 9. Basic Statistical Methods and Control Chart Principles -- Chapter 10. Usability and Presentation of Information -- Chapter 11. Advanced Analytics in Healthcare -- Chapter 12. Becoming an Analytical Healthcare Organization.
  • "Preface The aim of this book is to present in a single volume an overview and latest developments in time-to-event interval-censored methods along with application of such methods. The book is divided into three parts. Part I provides an introduction and overview of time-to-event methods for interval-censored data. Methodology is presented in Part II. Applications and related software appear in Part III. Part I consists of two chapters. In Chapter 1, Sun and Li present an overview of recent developments, with attention to nonparametric estimation and comparison of survival functions, regression analysis, analysis of multivariate clustered- and analysis of competing risks interval-censored data. In Chapter 2, Yu and Hsu provide a review of models for interval-censored (IC) data, including: independent interval censorship models, the full likelihood model, various models for C1, C2, and MIC data as well as multivariate IC models. Part II consists of seven chapters (3-9). Chapters 3, 4 and 5 deal with interval-censored methods for current status data. In Chapter 3, Banerjee presents: likelihood based inference, more general forms of interval censoring, competing risks, smoothed estimators, inference on a grid, outcome misclassi- cation, and semiparametric models. In Chapter 4, Zhang presents regression analyses using the proportional hazards model, the proportional odds model, and a linear transformation model, as well as considering bivariate current status data with the proportional odds model. In Chapter 5, Kim, Kim, Nam and Kim develop statistical analysis methods for dependent current status data and utilize the R Package CSD to analyze such data"--Provided by publisher.
  • "While the public health philosophy of the 20th Century -- emphasizing prevention -- is ideal for addressing natural disease outbreaks, it is not sufficient to confront 21st Century threats where adversaries may use biological weapons agents as part of a long-term campaign of aggression and terror. Health care providers and public health officers are among our first lines of defense. Therefore, we are building on the progress of the past three years to further improve the preparedness of our public health and medical systems to address current and future BW [biological warfare] threats and to respond with greater speed and flexibility to multiple or repetitive attacks." Homeland Security Presidential Directive 21 Bioterrorism is not a new threat in the 21st century -- thousands of years ago the plague and other contagious diseases were used in warfare -- but today the potential for catastrophic outcomes is greater than it has ever been. To address this threat, the medical and public health communities are putting various measures in place, including systems designed to pro-actively mon- itor populations for possible disease outbreaks"--Provided by publisher.
  • ch. 1. The Evolution of survey process quality -- ch. 2. The Survey process and data quality -- ch. 3. Coverage and nonresponse error -- ch. 4. The Measurement process and its implications for questionnaire design -- ch. 5. Errors due to interviewers and interviewing -- ch. 6. Data collection modes and associated errors -- ch. 7. Data processing: errors and their control -- ch. 8. Overview of survey error evaluation methods -- ch. 9. Sampling error -- ch. 10. Practical survey design for minimizing total survey error.
  • JMP for basic univariate and multivariate statistics books24x7, SUNet ID login required.
  • Kalman filter primer 2006, CRCnetBASE
  • Laboratory Statistics: Handbook of Formulas and Terms presents common strategies for comparing and evaluating numerical laboratory data. In particular, the text deals with the type of data and problems that laboratory scientists and students in analytical chemistry, clinical chemistry, epidemiology, and clinical research face on a daily basis. This book takes the mystery out of statistics and provides simple, hands-on instructions in the format of everyday formulas. As far as possible, spreadsheet shortcuts and functions are included, along with many simple worked examples. This book is a must-have guide to applied statistics in the lab that will result in improved experimental design and analysis. Comprehensive coverage of simple statistical concepts familiarizes the reader with formatted statistical expressionSimple, worked examples make formulas easy to use in real lifeSpreadsheet functions demonstrate how to find immediate solutions to common problemsIn-depth indexing and frequent use of synonyms facilitate the quick location of appropriate procedures.
  • Tutorial A: Getting started with SAS Enterprise Guide -- Tutorial B: Creating reports -- Tutorial C: Working with data in the query builder -- Tutorial D: Joining two data tables together -- Ch. 1: SAS Enterprise Guide basics -- Ch. 2: Bringing data into a project -- Ch. 3: Changing the way data values are displayed -- Ch. 4: Modifying data using the query builder -- Ch. 5: Sorting and filtering data -- Ch. 6: Combining data tables -- Ch. 7: Producing simple list and summary reports -- Ch. 8: Producing complex reports in summary tables -- Ch. 9: Basic statistical analysis -- Ch. 10: Producing graphs -- Ch. 11: Changing result styles and formats -- Ch. 12: Adding flexibility with prompts and conditions.
  • Machine generated contents note: -- 1. Using R -- R for Machine Learning -- Downloading and Installing R -- IDEs and Text Editors -- Loading and Installing R Packages -- R Basics for Machine Learning -- Further Reading on R -- 2. Data Exploration -- Exploration versus Confirmation -- What Is Data? -- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Visualizing the Relationships Between Columns -- 3. Classification: Spam Filtering -- This or That: Binary Classification -- Moving Gently into Conditional Probability -- Writing Our First Bayesian Spam Classifier -- Defining the Classifier and Testing It with Hard Ham -- Testing the Classifier Against All Email Types -- Improving the Results -- 4. Ranking: Priority Inbox -- How Do You Sort Something When You Don't Know the Order? -- Ordering Email Messages by Priority --Contents note continued: Priority Features of Email -- Writing a Priority Inbox -- Functions for Extracting the Feature Set -- Creating a Weighting Scheme for Ranking -- Weighting from Email Thread Activity -- Training and Testing the Ranker -- 5. Regression: Predicting Page Views -- Introducing Regression -- The Baseline Model -- Regression Using Dummy Variables -- Linear Regression in a Nutshell -- Predicting Web Traffic -- Defining Correlation -- 6. Regularization: Text Regression -- Nonlinear Relationships Between Columns: Beyond Straight Lines -- Introducing Polynomial Regression -- Methods for Preventing Overfitting -- Preventing Overfitting with Regularization -- Text Regression -- Logistic Regression to the Rescue -- 7. Optimization: Breaking Codes -- Introduction to Optimization -- Ridge Regression -- Code Breaking as Optimization -- 8. PCA: Building a Market Index -- Unsupervised Learning -- 9. MDS: Visually Exploring US Senator Similarity --Contents note continued: Clustering Based on Similarity -- A Brief Introduction to Distance Metrics and Multidirectional Scaling -- How Do US Senators Cluster? -- Analyzing US Senator Roll Call Data (101st--111th Congresses) -- 10. kNN: Recommendation Systems -- The k-Nearest Neighbors Algorithm -- R Package Installation Data -- 11. Analyzing Social Graphs -- Social Network Analysis -- Thinking Graphically -- Hacking Twitter Social Graph Data -- Working with the Google SocialGraph API -- Analyzing Twitter Networks -- Local Community Structure -- Visualizing the Clustered Twitter Network with Gephi -- Building Your Own "Who to Follow" Engine -- 12. Model Comparison -- SVMs: The Support Vector Machine -- Comparing Algorithms.
  • Making sense of data 2007, books24x7, SUNet ID login required.
  • v. I. Cellular biophysics, regulatory networks, development, biomedicine, and data analysis -- v. 2. Epidemiology, evolution and ecology, immunology, neural systems and the brain, and innovative mathematical methods and education.

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