Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results. In th
Tests If Dropouts Are Missed at Random
โ Scribed by Joachim Listing; Rainer Schlittgen
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 125 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0323-3847
No coin nor oath required. For personal study only.
โฆ Synopsis
Dropouts are a common problem in longitudinal investigations where individuals are measured repeatedly over time. This holds also in a study on rheumatoid arthritis where an inception cohort was followed up over three years. The question arose whether or not these individuals caused a selection bias. Two tests taken from the literature could be used to answer this question. But since they do not use all available information, a new asymptotic test is proposed. In a small simulation study it is shown that the new test is more powerful than the others.
๐ SIMILAR VOLUMES
Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. A simple and convenient approach to analysis is to develop summary measures for each individual and then regress the summary measures on between-subject covariates