Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science

Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wilcoxon-Mann-Whitney tests and Kaplan-Meier estimates. Examples, exercises, and the R package asht support practical use.

Author: Michael P. Fay, Erica H. Brittain
Publisher: Cambridge University Press
Published: 05/05/2022
Binding Type: Hardcover
Weight: 2.19lbs
Size: 10.00h x 7.20w x 1.16d
ISBN: 9781108423564

About the Author
Fay, Michael P.: - Michael P. Fay is a Mathematical Statistician at the National Institute of Allergy and Infectious Diseases, and previously worked at the National Cancer Institute. He has served as associate editor for Biometrics, and is currently an associate editor for Clinical Trials and a Fellow of the American Statistical Association. He is a co-author on over 100 papers in statistical and medical journals and has written and maintains over a dozen R packages on CRAN.Brittain, Erica: - Erica H. Brittain is Deputy Branch Chief of Biostatistics Research at the National Institute of Allergy and Infectious Diseases and has well over three decades of experience as a statistician, with previous positions at FDA, National Heart, Lung, and Blood Institute, and a statistical consulting company. Her applied work at NIH and her methodological publications in statistical journals focus on innovation in clinical trial design. She frequently serves on advisory panels for FDA and NIH, and has served as Statistical Consultant for Nature journals and Associate Editor for Controlled Clinical Trials.