Analysis of Multiple Longitudinal Outcomes and Multiplicity Adjustment
Application of Quasi-Least Squares and Gatekeeping Strategies in Clinical Trials
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Clinical trials often involve multiple endpoints that are clinically meaningful to determine efficacy and safety of the investigational medicinal product. These multiple outcomes are often measured over scheduled and/or unscheduled visits which add complexity when analyzing these types of data. This book is developed specifically to deal with multiple longitudinal outcomes in the framework of generalized estimating equations and their post-hoc multiplicity adjustment for the control of familywise error rate. The book covers a recent development and its extension of quasi-least squares to the a...
Clinical trials often involve multiple endpoints that are clinically meaningful to determine efficacy and safety of the investigational medicinal product. These multiple outcomes are often measured over scheduled and/or unscheduled visits which add complexity when analyzing these types of data. This book is developed specifically to deal with multiple longitudinal outcomes in the framework of generalized estimating equations and their post-hoc multiplicity adjustment for the control of familywise error rate. The book covers a recent development and its extension of quasi-least squares to the application in clinical trials for the analysis of single and multiple longitudinal outcomes. A user-written SAS software with its complete code and manual are also provided to fit various models described in this book. The book further discusses issues of inflation of type I error when multiple hypotheses are tested simultaneously and provides a number of practical solutions to control the familywise type I error using the gatekeeping strategies that are emerging as an important direction of improvement in the analysis of clinical trial data.
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