Statistical thinking for non-statisticians in drug regulation / Richard Kay

Basic ideas in clinical trial design -- Sampling and inferential statistics -- Confidence intervals and p-values -- Tests for simple treatment comparisons -- Adjusting the analysis -- Regression and analysis of covariance -- Intention-to-treat and analysis sets -- Power and sample size -- Statistical significance and clinical importance -- Multiple testing -- Non-parametric and related methods -- Equivalence and non-inferiority -- The analysis of survival data -- Interim analysis and data monitoring committees -- Bayesian statistics -- Adaptive designs -- Observational studies -- Meta-analysis -- Methods for the safety analysis and safety monitoring -- Diagnosis -- The role of statistics and statisticians

Available to OhioLINK libraries

Statistical methods in the pharmaceutical industry are accepted as a key element in the design and analysis of clinical studies. Increasingly, the medical and scientific community are aligning with the regulatory authorities and recognizing that correct statistical methodology is essential as the basis for valid conclusions. In order for those correct and robust methods to be successfully employed there needs to be effective communication across disciplines at all stages of the planning, conducting, analyzing and reporting of clinical studies associated with the development and evaluation of new drugs and devices. Statistical Thinking for Non-Statisticians in Drug Regulation provides a comprehensive in-depth guide to statistical methodology for pharmaceutical industry professionals, including physicians, investigators, medical science liaisons, clinical research scientists, medical writers, regulatory personnel, statistical programmers, senior data managers and those working in pharmacovigilance