Two experts in multilevel and longitudinal power and sample size propose to create a book- length, peer-reviewed, scholarly work entitled Power and Sample Size for Multilevel and Longitudinal Designs in Health Research. The two investigators will share authorship with an expert team of statisticians specializing in sample size methods. Scientists will use the resulting book to design multilevel and longitudinal studies. Health outcomes, spatial, imaging, and 'omics research are typical big data applications that often use multilevel and longitudinal designs. The book will be useful to scientists designing studies for NIH funding, companies seeking FDA approval for drugs or devices, and laboratory researchers conducting basic research. No matter where they work, scientists must calculate sample size correctly, both for ethical reasons and because of regulatory and funding agency requirements. The book entitled Power and Sample Size for Multilevel and Longitudinal Designs in Health Research will provide power and sample size methods for multilevel and longitudinal designs in an accessible, readable format packed with examples of power analyses. In the proposed book, the authors will: 1) Teach scientists how to plan multilevel and longitudinal data and sample size analysis at the same time; 2) Use clear language and step-by- step instructions to show scientists how to do power and sample size analysis for any multilevel and longitudinal design; 3) Describe practical techniques for selecting inputs to implement sample size analysis; and 4) Use an authorship and peer-review process common to scientific journals to increase the quality and readability of the book. Market research shows that there is an eager, large and multidisciplinary scientific audience for the book. The enthusiasm of the scientists stems from their ethical and professional needs to choose sample size correctly for multilevel and longitudinal studies, as well as the lack of accessible and accurate methods in currently available books and software.
The book-length, peer-reviewed scholarly work entitled 'Power and Sample Size for Multilevel and Longitudinal Designs in Health Research' fills a documented need for the first translation of state-of-the-art multilevel and longitudinal power and sample size methods into accessible language. A multiple author, multi-step peer-reviewed writing process will ensure that the book combines the highest standard of technical excellence, the most up-to-date statistical methods, and a consistent level, style, and voice. The book will provide thorough coverage of sample size methods for multilevel and longitudinal studies. Such studies are increasingly common in health research, especially in big data areas such as spatial research, imaging, 'omics, and health systems outcomes research.
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| Johnson, Jacqueline L; Kreidler, Sarah M; Catellier, Diane J et al. (2015) Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes. Stat Med 34:3531-45 |
