alexa Current State Of Statistical Methods In Handling Missing Data In Clinical Trials
ISSN: 2167-0870

Journal of Clinical Trials
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4th International Conference on Clinical Trials
September 11-13, 2017 San Antonio, USA

Richard Entsuah
Merck Research Laboratories, USA
ScientificTracks Abstracts: J Clin Trials
DOI: 10.4172/2167-0870-C1-019
Abstract
The issue of missing data in clinical trials seems to be an ongoing challenge resulting in different statistical methods that have been proposed to deal this ongoing problem. We shall discuss mitigation strategies to prevent missing data which can help minimize dropout rates. This workshop will provide an overview of various methods that have been adopted by applied statisticians in drug submissions in recent years. The focus will be on longitudinal continuous data using both parametric and non-parametric methods. The choice of primary estimands is gaining lots of attention in the filed recently and we shall discuss these points. The concept of missing at Random (MAR) and missing not at random (MNAR) which has gained much attention in the last two decades will be discussed. This will include techniques like mixed model repeated measure (MMRM), selection models, pattern mixture models, jump to reference, dipping point, multiple imputations and ETRANKĀ®-A nonparametric method.
Biography

Richard Entsuah is a Fellow of the American Statistical Association. He completed his PhD from University of Michigan. He was an Assistant Professor of Biometry at University of Illinois in Chicago. He joined Wyeth Research from 1988 to 2007 and left as an Assistant Vice President of Global Biostatistics and Programming. He joined Merck Research Labs as Executive Director of Late Development Statistics and is the Research Group 4 Head for Neuroscience and Respiratory Immunology.

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