alexa An Analysis of Selection Models for Incomplete Longitudinal Clinical Trials Due to Dropout: An Application to Multi-centre Trial Data
ISSN: 2161-1165

Epidemiology: Open Access
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Research Article

An Analysis of Selection Models for Incomplete Longitudinal Clinical Trials Due to Dropout: An Application to Multi-centre Trial Data

Ali Satty*

Faculty of Mathematical Sciences and Statistics, Department of Statistics and Actuarial Science, Elneelain University, Khartoum, Sudan

Corresponding Author:
Ali Satty
Faculty of Mathematical Sciences and Statistics
Department of Statistics and Actuarial Science
Elneelain University, Khartoum, Sudan
E-mail: [email protected]

Received Date: July 13, 2015; Accepted Date: January 19, 2016; Published Date: January 26, 2016

Citation: Satty A (2016) An Analysis of Selection Models for Incomplete Longitudinal Clinical Trials Due to Dropout: An Application to Multi-centre Trial Data. Epidemiol 6:221. doi:10.4172/2161-1165.1000221

Copyright: © 2016 Satty A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

A common problem encountered in statistical analysis is that of missing data, which occurs when some variables have missing values in some units. The present paper deals with the analysis of longitudinal continuous measurements with incomplete data due to non-ignorable dropout. In repeated measurements data, as one solution to a problem, the selection model assumes a mechanism of outcome-dependent dropout and jointly both the measurement together with dropout process of repeated measures. We consider the construction of a particular type of selection model that uses a logistic regression model to describe the dependency of dropout indicators on the longitudinal measurement. We focus on the use of the Diggle-Kenward model as a tool for assessing the sensitivity of a selection model in terms of the modeling assumptions. Our main objective here is to investigate the influence on inference that might be exerted on the considered data by the dropout process. We restrict attention to a model for repeated Gaussian measures, subject to potentially non-random dropout. To investigate this, we carry out an application for analyzing incomplete longitudinal clinical trial with dropout by using a practical example in the form of a multi-centre clinical trial data.

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