Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

missing at random (MAR) Related Abstracts

2 A Review of Methods for Handling Missing Data in the Formof Dropouts in Longitudinal Clinical Trials

Authors: A. Satty, H. Mwambi


Much clinical trials data-based research are characterized by the unavoidable problem of dropout as a result of missing or erroneous values. This paper aims to review some of the various techniques to address the dropout problems in longitudinal clinical trials. The fundamental concepts of the patterns and mechanisms of dropout are discussed. This study presents five general techniques for handling dropout: (1) Deletion methods; (2) Imputation-based methods; (3) Data augmentation methods; (4) Likelihood-based methods; and (5) MNAR-based methods. Under each technique, several methods that are commonly used to deal with dropout are presented, including a review of the existing literature in which we examine the effectiveness of these methods in the analysis of incomplete data. Two application examples are presented to study the potential strengths or weaknesses of some of the methods under certain dropout mechanisms as well as to assess the sensitivity of the modelling assumptions.

Keywords: Sensitivity Analysis, incomplete longitudinal clinical trials, missing at random (MAR), imputation, weighting methods

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1 Survival Data with Incomplete Missing Categorical Covariates

Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar


The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: Weibull distribution, EM algorithm, missing at random (MAR), incomplete categorical covariates, ignorable missing data

Procedia PDF Downloads 248