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Statistics

  • 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.
  • 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.
  • "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."Although the popularity of the Bayesian approach to statistics has been growing rapidly for many years, among those working in business and industry there are still many who think of it as somewhat esoteric, not focused on practical issues, or generally quite difficult to understand. This view may be partly due to the relatively few books that focus primarily on how to apply Bayesian methods to a wide range of common problems. I believe that the essence of the approach is not only much more relevant to the scientific problems that require statistical thinking and methods, but also much easier to understand and explain to the wider scientific community. But being convinced of the benefits of the Bayesian approach is not enough if the person charged with analyzing the data does not have the computing software tools to implement these methods. Although WinBUGS (Lunn et al. 2000) provides sufficient functionality for the vast majority of data analyses that are undertaken, there is still a steep learning curve associated with the programming language that many will not have the time or motivation to overcome. This book describes a graphical user interface (GUI) for WinBUGS, BugsXLA, the purpose of which is to make Bayesian analysis relatively simple. Since I have always been an advocate of Excel as a tool for exploratory graphical analysis of data (somewhat against the anti-Excel feelings in the statistical community generally), I created BugsXLA as an Excel add-in. Other than to calculate some simple summary statistics from the data, Excel is only used as a convenient vehicle to store the data, plus some meta-data used by BugsXLA, as well as a home for the Visual Basic program itself"--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 disease mapping 2009, CRCnetBASE
    Bayesian inference and modeling -- Computational issues -- Residuals and goodness-of-fit -- Disease map reconstruction and relative risk estimation -- Disease cluster detection -- Ecological analysis -- Multiple scale analysis -- Multivariate disease analysis -- Spatial survival and longitudinal analysis -- Spatiotemporal disease mapping.
  • 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.
  • 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.
  • 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.
  • "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.
  • 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.
  • 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
  • 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.
  • 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
  • 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.
  • MATLAB for psychologists 2012, Springer
    Basic operations -- Data handling -- Plotting data -- Start programming -- A better sound -- Create and process images -- Data analysis -- The charm of graphical user interface -- Psychtoolbox : video -- Psychtoolbox : sound, keyboard and mouse.
  • MATLAB. 3rd ed. 2013, ScienceDirect
    pt. 1. Introduction to programming using MATLAB -- Introduction to Matlab -- Vectors and matrices -- Introduction to MATLAB programming -- Selection statements -- Loop statements and vectorizing code -- MATLAB programs -- String manipulation -- Data structures : cell arrays and structures -- Advanced file input and output -- Advanced functions -- pt. 2. Advanced topics for problem solving with MATLAB -- Advanced plotting techniques -- Basic statistics, sets, sorting, and indexing -- Sights and sounds -- Advanced mathematics.
  • "Emphasizing applications over theory, this book provides a comprehensive survey of this method and provides readers with standards and directions on how to run sound clinical and other types of studies. The author clearly explains how to reduce measurement error and presents numerous practical examples of the interobserver agreement approach. To help with problem solving, he includes SAS code, both within the book and on the CRC website. An extensive review of the literature offers access to the latest developments in the field. This edition presents new applications, new tables, more detail on SAS, new code, updated references, and two new chapters"--Provided by publisher.
  • Part 1. Challenges -- The Global Burden of Infectious Diseases / Paulo Pinheiro, Colin D. Mathers and Alexander Krämer -- Global Challenges of Infectious Disease Epidemiology / Alexander Krämer and Md. Mobarak Hossain Khan -- Emerging and Re-emerging Infectious Diseases / Thomas Löscher and Luise Prüfer-Krämer -- Infectious Disease Control Policies and the Role of Governmental and Intergovernmental Organisations / Gérard Krause -- Part 2. General concepts and methods -- Principles of Infectious Disease Epidemiology / Alexander Krämer, Manas Akmatov and Mirjam Kretzschmar -- Social Risk Factors / Klaus Krickeberg and David Klemperer -- Molecular Typing and Clustering Analysis as a Tool for Epidemiology of Infectious Diseases / Sylvia M. Bruisten and Leo Schouls -- Epidemiologic Surveillance / Ralf Reintjes and Klaus Krickeberg -- Outbreak Investigations / Ralf Reintjes and Aryna Zanuzdana -- Geographic Information Systems / Patrick Hostert and Oliver Gruebner -- Methods and Concepts of Epidemiology / Rafael Mikolajczyk -- Mathematical Models in Infectious Disease Epidemiology / Mirjam Kretzschmar and Jacco Wallinga -- Immunity to Infectious Diseases / Timo Ulrichs -- Principles and Practice of Vaccinology / Richard Pebody and Mirjam Kretzschmar -- Health Economics of Infectious Diseases / Robert Welte, Reiner Leidl, Wolfgang Greiner and Maarten Postma -- Part 3. Epidemiology of particular infectious diseases -- Airborne Transmission: Influenza and Tuberculosis / Timo Ulrichs -- Infectious Childhood Diarrhea in Developing Countries / Charles P. Larson, Lars Henning, Stephen Luby and Asg Faruque -- Bloodborne and Sexual Transmission: HIV/AIDS / Charlotte van den Berg, Karen Lindenburg and Roel Coutinho -- Blood Borne and Sexual Transmission: Hepatitis B and C / Freke Zuure, Susan Hahné, Thijs van de Laar, Maria Prins and Jim van Steenbergen -- Sexual Transmission: Chlamydia trachomatis / Robert E. Johnson and Stuart M. Berman -- Vector-Borne Transmission: Malaria, Dengue, and Yellow Fever / Tomas Jelinek -- Nosocomial Transmission: Methicillin-Resistant Staphylococcus aureus (MRSA) / M.J.M. Bonten and M.C.J. Bootsma -- Infectious Diseases and Cancer: HPV / Helen Trottier and Eduardo L. Franco.
  • 1. Multiplicity problems in clinical trials : a regulatory perspective / Mohammad Huque and Joachim Rèohmel -- 2. Multiple testing methodology / Alex Dmitrienko ... [et al.] -- 3. Multiple testing in dose-response problems / Frank Bretz, Ajit C. Tamhane, and Josâe Pinheiro -- 4. Analysis of multiple endpoints in clinical trials / Ajit C. Tamhane and Alex Dmitrienko -- 5. Gatekeeping procedures in clinical trials / Alex Dmitrienko and Ajit C. Tamhane -- 6. Adaptive designs and confirmatory hypothesis testing / Willi Maurer, Michael Branson, and Martin Posch -- 7. Design and analysis of microarray experiments for pharmacogenomics / Jason C. Hsu ... [et al.].
  • Machine generated contents note:2.1.Introduction --2.2.Continuous/interval scale data --2.3.Simple and multiple linear regression models --2.4.Checking assumptions in linear regression models --2.5.Likelihood: multiple linear regression --2.6.Comparing model likelihoods --2.7.Application of a multiple linear regression model --2.8.Exercises on linear models --3.1.Binary data --3.1.1.Introduction --3.1.2.Logistic regression --3.1.3.Logit and probit transformations --3.1.4.General logistic regression --3.1.5.Likelihood --3.1.6.Example with binary data --3.2.Ordinal data --3.2.1.Introduction --3.2.2.The ordered logit model --3.2.3.Dichotomization of ordered categories --3.2.4.Likelihood --3.2.5.Example with ordered data --3.3.Count data --3.3.1.Introduction --3.3.2.Poisson regression models --3.3.3.Likelihood --3.3.4.Example with count data --3.4.Exercises --4.1.Introduction --4.2.The linear model4.3.The binary response model --4.4.The Poisson model --4.5.Likelihood --5.1.Introduction --5.2.Linear mixed model --5.3.The intraclass correlation coefficient --5.4.Parameter estimation by maximum likelihood --5.5.Regression with level-two effects --5.6.Two-level random intercept models --5.7.General two-level models including random intercepts --5.8.Likelihood --5.9.Residuals --5.10.Checking assumptions in mixed models --5.11.Comparing model likelihoods --5.12.Application of a two-level linear model --5.13.Two-level growth models --5.13.1.A two-level repeated measures model --5.13.2.A linear growth model --5.13.3.A quadratic growth model --5.14.Likelihood --5.15.Example using linear growth models --5.16.Exercises using mixed models for continuous/interval scale data --6.1.Introduction --6.2.The two-level logistic model --6.3.General two-level logistic models --6.4.Intraclass correlation coefficient --6.5.Likelihood --6.6.Example using binary data --6.7.Exercises using mixed models for binary data7.1.Introduction --7.2.The two-level ordered logit model --7.3.Likelihood --7.4.Example using mixed models for ordered data --7.5.Exercises using mixed models for ordinal data --8.1.Introduction --8.2.The two-level Poisson model --8.3.Likelihood --8.4.Example using mixed models for count data --8.5.Exercises using mixed models for count data --9.1.Introduction --9.2.The mixed linear model --9.3.The mixed binary response model --9.4.The mixed Poisson model --9.5.Likelihood --10.1.Introduction --10.2.Three-level random intercept models --10.3.Three-level generalized linear models --10.4.Linear models --10.5.Binary response models --10.6.Likelihood --10.7.Example using three-level generalized linear models --10.8.Exercises using three-level generalized linear mixed models --11.1.Introduction --11.2.Multivariate two-level generalized linear model --11.3.Bivariate Poisson model: example --11.4.Bivariate ordered response model: example --11.5.Bivariate linear-probit model: example --11.6.Multivariate two-level generalized linear model likelihood11.7.Exercises using multivariate generalized linear mixed models --12.1.Introduction --12.1.1.Left censoring --12.1.2.Right censoring --12.1.3.Time-varying explanatory variables --12.1.4.Competing risks --12.2.Duration data in discrete time --12.2.1.Single-level models for duration data --12.2.2.Two-level models for duration data --12.2.3.Three-level models for duration data --12.3.Renewal data --12.3.1.Introduction --12.3.2.Example: renewal models --12.4.Competing risk data --12.4.1.Introduction --12.4.2.Likelihood --12.4.3.Example: competing risk data --12.5.Exercises using renewal and competing risks models --13.1.Introduction --13.2.Mover-stayer model --13.3.Likelihood incorporating the mover-stayer model --13.4.Example 1: stayers within count data --13.5.Example 2: stayers within binary data --13.6.Exercises: stayers --14.1.Introduction to key issues: heterogeneity, state dependence and non-stationarity --14.2.Example --14.3.Random effects models --14.4.Initial conditions problem --14.5.Initial treatment14.6.Example: depression data --14.7.Classical conditional analysis --14.8.Classical conditional model: example --14.9.Conditioning on initial response but allowing random effect uol to be dependent on z3 --14.10.Wooldridge conditional model: example --14.11.Modelling the initial conditions --14.12.Same random effect in the initial response and subsequent response models with a common scale parameter --14.13.Joint analysis with a common random effect: example --14.14.Same random effect in models of the initial response and subsequent responses but with different scale parameters --14.15.Joint analysis with a common random effect (different scale parameters): example --14.16.Different random effects in models of the initial response and subsequent responses --14.17.Different random effects: example --14.18.Embedding the Wooldridge approach in joint models for the initial response and subsequent responses --14.19.Joint model incorporating the Wooldridge approach: example --14.20.Other link functions --14.21.Exercises using models incorporating initial conditions/state dependence in binary data15.1.Introduction --15.2.Fixed effects treatment of the two-level linear model --15.3.Dummy variable specification of the fixed effects model --15.4.Empirical comparison of two-level fixed effects and random effects estimators --15.5.Implicit fixed effects estimator --15.6.Random effects models --15.7.Comparing two-level fixed effects and random effects models --15.8.Fixed effects treatment of the three-level linear model --15.9.Exercises comparing fixed effects and random effects --A.1.SabreR installation --A.2.SabreR commands --A.2.1.The arguments of the SabreR object --A.2.2.The anatomy of a SabreR command file --A.3.Quadrature --A.3.1.Standard Gaussian quadrature --A.3.2.Performance of Gaussian quadrature --A.3.3.Adaptive quadrature --A.4.Estimation --A.4.1.Maximizing the log likelihood of random effects models --A.5.Fixed effects linear models --A.6.Endogenous and exogenous variables --B.1.Getting started with R --B.1.1.Preliminaries --B.1.1.1.Working with R in interactive mode --B.1.1.2.Basic functions --B.1.1.3.Getting helpB.1.1.4.Stopping R --B.1.2.Creating and manipulating data --B.1.2.1.Vectors and lists --B.1.2.2.Vectors --B.1.2.3.Vector operations --B.1.2.4.Lists --B.1.2.5.Data frames --B.1.3.Session management --B.1.3.1.Managing objects --B.1.3.2.Attaching and detaching objects --B.1.3.3.Serialization --B.1.3.4.R scripts --B.1.3.5.Batch processing --B.1.4.R packages --B.1.4.1.Loading a package into R --B.1.4.2.Installing a package for use in R --B.1.4.3.R and Statistics --B.2.Data preparation for SabreR --B.2.1.Creation of dummy variables --B.2.2.Missing values --B.2.3.Creating lagged response covariate data.
  • New drug development -- The regulatory environment -- Drug discovery -- Nonclinical research -- Designing clinical trials -- Conducting clinical trials I: Experimental methodology -- Conducting clinical trials II: Operational execution -- Statistical analysis -- Statistical significance -- Clinical significance -- Sample size estimation -- General safety assessments -- Efficacy assessment -- Cardiac and cardiovascular safety assessments -- Manufacturing small molecule drugs and biologicals -- Postmarketing surveillance -- Main themes and concluding comments -- References -- Index.
  • Pharmaceutical statistics using SAS-- a practical guide books24x7, SUNet ID login required.
    Fulltext ProQuest Safari
    Statistics in drug development by Christy Chuang-Stein and Ralph D'Agostino -- Modern classification methods for drug discovery by Kjell Johnson and William Rayens -- Model building techniques in drug discovery by Kimberly Crimin and Thomas Vidmar -- Statistical considerations in analytical method validation by Bruno Boulanger, Viswanath Devanaryan, Walthère Dewé, and Wendell Smith -- Some statistical considerations in nonclinical safety assessment by Wherly Hoffman, Cindy Lee, Alan Chiang, Kevin Guo, and Daniel Ness -- Nonparametric methods in pharmaceutical statistics by Paul Juneau -- Optimal design of experiments in pharmaceutical applications by Valerii Fedorov, Robert Gagnon, Sergei Leonov, and Yuehui Wu -- Analysis of human pharmacokinetic data by Scott Patterson and Brian Smith -- Allocation in randomized clinical trials by Olga Kuznetsova and Anastasia Ivanova -- Sample-size analysis for traditional hypothesis testing: concepts and issues by Ralph G. O'Brien and John Castelloe -- Design and analysis of dose-ranging clinical studies by Alex Dmitrienko, Kathleen Fritsch, Janson Hsu, and Stephen Ruberg -- Analysis of incomplete data by Geert Molenberghs, Caroline Beunckens, Herbert Thijs, Ivy Jansen, Geert Verbeke, Michael Kenward, and Kristen Van Steen -- Reliability and validity: Assessing the psychometric properties of rating scales by Douglas Faries and Ilker Yalcin -- Decision analysis in drug development by Carl-Fredrik Burman, Andy Grieve, and Stephen Senn.
  • R cookbook. 1st ed. 2011, ProQuest Safari
  • R graphs cookbook 2011, ProQuest Safari

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