Confidence Intervals for Discrete Data in Clinical Research is designed as a toolbox for biomedical researchers. Analysis of discrete data is one of the most used yet vexing areas in clinical research. The array of methodologies available in the literature to address the inferential questions for binomial and multinomial data can be a double-edged sword. On the one hand, these methods open a rich avenue of exploration of data; on the other, the wide-ranging and competing methodologies potentially lead to conflicting inferences, adding to researchers' confusion and frustration and also leading to reporting bias. This book addresses the problems that many practitioners experience in choosing and implementing fit for purpose data analysis methods to answer critical inferential questions for binomial and count data. The book is an outgrowth of the authors' collective experience in biomedical research and provides an excellent overview of inferential questions of interest for binomial proportions and rates based on count data, and reviews various solutions to these problems available in the literature. Each chapter discusses the strengths and weaknesses of the methods and suggests practical recommendations. The book's primary focus is on applications in clinical research, and the goal is to provide direct benefit to the users involved in the biomedical field.

There is only one published book on confidence interval for clinical research. This book has a cookbook style with several examples and codes so that methods presented in the book can be implemented. The primary audience will be statisticians.

Statistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned, mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ’ principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V’ principle provides a simple estimator of the log odds ratio that is ideally suited for stratified analysis with a binary outcome. The same principle can be used to estimate the log hazard ratio and facilitate stratified analysis in a survival setting. Learn these and other simple techniques that will make you an invaluable clinical trial statistician.

Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials provides a practical introduction to unconditional approaches to planning randomised clinical trials, particularly aimed at drug development in the pharmaceutical industry. This book is aimed at providing guidance to practitioners in using average power, assurance and related concepts. This book brings together recent research and sets them in a consistent framework and provides a fresh insight into how such methods can be used. Features: A focus on normal theory linking average power, expected power, predictive power, assurance, conditional Bayesian power and Bayesian power. Extensions of the concepts to binomial, and time-to-event outcomes and non-inferiority trials An investigation into the upper bound on average power, assurance and Bayesian power based on the prior probability of a positive treatment effect Application of assurance to a series of trials in a development program and an introduction of the assurance of an individual trial conditional on the positive outcome of an earlier trial in the program, or to the successful outcome of an interim analysis Prior distribution of power and sample size Extension of the basic approach to proof-of-concept trials with dual success criteria Investigation of the connection between conditional and predictive power at an interim analysis and power and assurance Introduction of the idea of surety in sample sizing of clinical trials based on the width of the confidence intervals for the treatment effect, and an unconditional version.

This highly popular introduction to confidence intervals has been thoroughly updated and expanded. It includes methods for using confidence intervals, with illustrative worked examples and extensive guidelines and checklists to help the novice.

"Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases."--Publisher's description.

Cancer is a dreaded disease. One in two people will be diagnosed with cancer within their lifetime. Medical Statistics for Cancer Studies shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It gives some background in cancer biology and genetics, followed by detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics. It includes lots of examples using real data from the author’s many years of experience working in a cancer clinical trials unit. Features: A broad and accessible overview of statistical methods in cancer research Necessary background in cancer biology and genetics Details of statistical methodology with minimal algebra Many examples using real data from cancer clinical trials Appendix giving statistics revision.