Preface.PART I FOUNDATIONS.1. Sources of Error.Prescription.Fundamental Concepts.Ad Hoc, Post Hoc Hypotheses.2. Hypotheses: The Why of Your Research.Prescription.What Is a Hypothesis?How precise must a hypothesis be?Found Data.Null hypothesis.Neyman-Pearson Theory.Deduction and Induction.Losses.Decisions.To Learn More.3. Collecting Data.Preparation.Measuring Devices.Determining Sample Size.Fundamental Assumptions.Experimental Design.Four Guidelines.Are Experiments Really Necessary?To Learn More.PART II HYPOTHESIS TESTING AND ESTIMATION.4. Estimation.Prevention.Desirable and Not-So-Desirable Estimators.Interval Estimates.Improved Results.Summary.To Learn More.5. Testing Hypotheses: Choosing a Test Statistic.Comparing Means of Two Populations.Comparing Variances.Comparing the Means of K Samples.Higher-Order Experimental Designs.Contingency Tables.Inferior Tests.Multiple Tests.Before You Draw Conclusions.Summary.To Learn More.6. Strengths and Limitations of Some Miscellaneous Statistical Procedures.Bootstrap.Bayesian Methodology.Meta-Analysis.Permutation Tests.To Learn More.7. Reporting Your Results.Fundamentals.Tables.Standard Error.p-Values.Confidence Intervals.Recognizing and Reporting Biases.Reporting Power.Drawing Conclusions.Summary.To Learn More.8. Interpreting Reports.With A Grain of Salt.Rates and Percentages.Interpreting Computer Printouts.9. Graphics.The Soccer Data.Five Rules for Avoiding Bad Graphics.One Rule for Correct Usage of Three-Dimensional Graphics.The Misunderstood Pie Chart.Two Rules for Effective Display of Subgroup Information.Two Rules for Text Elements in Graphics.Multidimensional Displays.Choosing Graphical Displays.Summary.To Learn More.PART III BUILDING A MODEL.10. Univariate Regression.Model Selection.Estimating Coefficients.Further Considerations.Summary.To Learn More.11. Alternate Methods of Regression.Linear vs. Nonlinear Regression.Least Absolute Deviation Regression.Errors-in-Variables Regression.Quantile Regression.The Ecological Fallacy.Nonsense Regression.Summary.To Learn More.12. Multivariable Regression.Caveats.Factor Analysis.General Linearized Models.Reporting Your Results.A Conjecture.Decision Trees.Building a Successful Model.To Learn More.13. Validation.Methods of Validation.Measures of Predictive Success.Long-Term Stability.To Learn More.Appendix A.Appendix B.Glossary, Grouped by Related but Distinct Terms.Bibliography.Author Index.Subject Index.
Preface. 1. Variation (or What Statistics Is All About). 2. Probability. 3. Distributions. 4. Testing Hypotheses. 5. Designing an Experiment or Survey. 6. Analyzing Complex Experiments. 7. Developing Models. 8. Reporting Your Findings. 9. Problem Solving. Appendix: An Microsoft Office Excel Primer. Index to Excel and Excel Add-In Functions. Subject Index.
Preface. 1. Very Large Arrays. 2. Permutation Tests. 3. Applying the Permutation Test. 4. Gathering and Preparing Data for Analysis. 5. Multiple Tests. 6. Bootstrap. 7. Classification Methods. 8. Applying Decision Trees. Glossary: Biological Terms. Glossary: Statistical Terms. Appendix: An R Primer. Bibliography. Author Index Subject Index.
This newly updated edition of the benchmark guide to computer-assisted clinical trials provides a comprehensive primer for prospective managers. It covers every critical issue of the design and conduct of clinical trials, including study design, organization, regulatory agency liaison, data collection and analysis, as well as recruitment, software, monitoring, and reporting. Keeping the same user-friendly format as the original, this Second Edition features new examples and the latest developments in regulatory guidelines, such as e-submission procedures and computerized direct data acquisition. The new edition also reflects the increasing globalization of clinical trial activities, and includes new information about international standards and procedures, including the Common Technical Document and CDISC standards. This step-by-step guide is supported by handy checklists and extracts from submitted protocols. Experienced author and consultant Phillip Good incorporateshumorous yet instructive anecdotes to illustrate common pitfalls. Based on the proven industrial formula of planning, implementing, and finally performing essential checks, the book's three sections-"Plan," "Do," and "Check"-includethe following material: * Should the trials be conducted? * Put it in the computer and keep it there * Staffing for success * Designing trials and determining sample size * Budgeting * Recruiting and retaining patients and physicians * Data management * Monitoring the trials * Data analysis * After action review * Exception handling Executive and managerial professionals involved in the design and analysis of clinical experiments, along with clinical research associates, biostatisticians, and students in public health will find A Manager's Guide an indispensable resource. Praise for the First Edition: ". . . readable, informative and at times witty . . . never stops being concise and well written . . . a book worth a read . . ." -Statistics in Medicine "The book is very prescriptive and full of lists and tables with which to guide managers in making effective decisions in using computer-assisted clinical trials in pharmaceutical studies." -Technometrics "This book is must-have reading for anyone in the business . . ." -Clinical Chemistry
Praise for the First Edition of Common Errors in Statistics " . . . let me recommend Common Errors to all those who interact with statistics, whatever their level of statistical understanding . . . " --Stats 40 " . . . written . . . for the people who define good practice rather than seek to emulate it." --Journal of Biopharmaceutical Statistics " . . . highly informative, enjoyable to read, and of potential use to a broad audience. It is a book that should be on the reference shelf of many statisticians and researchers." --The American Statistician " . . . I found this book the most easily readable statistics book ever. The credit for this certainly goes to Phillip Good." --E-STREAMS A tried-and-true guide to the proper application of statistics Now in a second edition, the highly readable Common Errors in Statistics (and How to Avoid Them) lays a mathematically rigorous and readily accessible foundation for understanding statistical procedures, problems, and solutions. This handy field guide analyzes common mistakes, debunks popular myths, and helps readers to choose the best and most effective statistical technique for each of their tasks. Written for both the newly minted academic and the professional who uses statistics in their work, the book covers creating a research plan, formulating a hypothesis, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, data mining, Bayes' Theorem, the bootstrap, and many other topics. The Second Edition has been extensively revised to include: * Additional charts and graphs * Two new chapters, Interpreting Reports and Which Regression Method? * New sections on practical versus statistical significance and nonuniqueness in multivariate regression * Added material from the authors' online courses at statistics.com * New material on unbalanced designs, report interpretation, and alternative modeling methods With a final emphasis on both finding solutions and the great value of statistics when applied in the proper context, this book is eminently useful to students and professionals in the fields of research, industry, medicine, and government.
Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." —CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.
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