Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described.
Provides a practical guide to the analysis of clinical trials and related studies with missing data.
Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.
Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.
Illustrated throughout with real-life case studies and worked examples from clinical trials.
Details the use and implementation of the necessary statistical software, primarily SAS.
Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.
Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described.
Provides a practical guide to the analysis of clinical trials and related studies with missing data.
Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.
Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.
Illustrated throughout with real-life case studies and worked examples from clinical trials.
Details the use and implementation of the necessary statistical software, primarily SAS.
Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.
Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases.Why is this a problem? Because most...
Volume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary...
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