"A few years ago I started a book by first writing a fairly extensive preface. I never finished that book and resolved that in the future I would write first the book and then the preface. Leo Breiman (1928-2005) -Preface to "Probability" (Breiman, 1968)When I eventually agreed to write a book on subgroup analyses I remembered the first paragraph of the preface that the late (and great) probabilist and statistician Leo Breiman added to his book "Probability," a classic textbook during my study days. I interpreted Leo's words as a warning to all potential authors not to start from the wrong end. Hence I postponed writing this part of the book if not to the very end but to the point when progress looked encouraging. This book is about a topic of intense research driven on one hand by the promises of precision medicine and on the other by the intention of regulating agencies to obtain information about the consistency of findings from clinical trials in drug applications. It can therefore be at best a snapshot of the state of the art at a given point in time from the author's perspective of the topic. To whom may the book concern? First, its main parts require a solid knowledge of statistical concepts like random variables, bias, variance, confidence intervals, and statistical tests, but also a background in statistical modeling, re-sampling, and model selection. Re-sampling iswell presented in "An Introduction to the Bootstrap" (Efron and Tibshirani, 1993) while "Statistical Learning with Sparsity" (Hastie et al., 2015) covers the modern aspects of modeling and model selection. On the practical side, knowledge about concepts of clinical trials and drug development like efficacy and safety, and randomization and blinding are helpful. "Statistical Issues inDrug Development" (Senn, 2007) covers many of these topics. Notwithstanding what is said above, parts of the book should be readable by a non-statistical audience, mainly the chapters on history and to a lesser extent on pitfalls. Chapters digging a bit deeper into methodology (those coming with a heavier load of equations) should be primarily appreciated by statisticians. With this in mind, clinicians and statisticians from the area of clinical development and regulation should benefit most, although the topic of subgroup analysis has a much wider scope"--
This essential guide on subgroup analyses in the emerging area of personalized medicine covers the issues of subgroup analyses from a practical and a theoretical/methodological point of view. The practical part introduces the issues using examples from the literature where subgroup analyses led to unexpected or difficult-to-interpret results, which have been interpreted differently by different stakeholders. On the technical side, the book addresses selection and selection bias variance reduction by borrowing information from the full population in estimating a subgroup effect. To this end, subgroup analysis will be linked to statistical modelling, and subgroup selection to model selection. This connection makes the techniques developed for model selection applicable to subgroup analysis.
Beginning with a history of subgroup analysis, Exploratory Subgroup Analyses in Clinical Research offers chapters that cover: objectives and current practice of subgroup analyses; pitfalls of subgroup analyses; subgroup analysis and modeling; hierarchical models in subgroup analysis; and selection bias in regression. It also looks at the predicted individual treatment effect and offers an outlook of the topic in its final chapter.
- Focuses on the statistical aspects of subgroup analysis
- Filled with classroom and conference-workshop tested material
- Written by a leading expert in the field of subgroup analysis
- Complemented with a companion website featuring downloadable datasets and examples for teaching use
Exploratory Subgroup Analyses in Clinical Research is an ideal book for medical statisticians and biostatisticians and will greatly benefit physicians and researchers interested in personalized medicine.
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