Recent decades have brought advances in statistical theory for missing data‎,‎ which‎,‎ combined with advances in computing ability‎,‎ have allowed implementation of a wide array of analyses‎.‎ In fact‎,‎ so many methods are available that it can be difficult to ascertain when to use which method‎.‎ This book focuses on the prevention and treatment of missing data in longitudinal clinical trials‎.‎ Based on his extensive experience with missing data‎,‎ the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct‎.‎ He offers a practical guide to key principles and explains analytic methods for the non‎-statistician using limited statistical notation and jargon‎.‎ The book‎'‎s goal is to present a comprehensive strategy for preventing and treating missing data‎,‎ and to make available the programs used to conduct the analyses of the example dataset‎.‎