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2.  Comments on: dynamic relations for sparsely sampled Gaussian processes 
Test (Madrid, Spain)  2010;19(1):50-53.
PMCID: PMC2999881  PMID: 21151714
3.  Missing data methods in longitudinal studies: a review 
Test (Madrid, Spain)  2009;18(1):1-43.
Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area. This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic tools. Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks; inferential paradigms; and sensitivity analysis frameworks. These are described in detail. A variety of concrete modeling devices is presented. To make matters concrete, two case studies are considered. The first one concerns quality of life among breast cancer patients, while the second one examines data from the Muscatine children’s obesity study.
PMCID: PMC3016756  PMID: 21218187
Expectation-maximization algorithm; Incomplete data; Missing completely at random; Missing at random; Missing not at random; Pattern-mixture model; Selection model; Sensitivity analyses; Shared-parameter model
4.  Comments on: A review on empirical likelihood methods for regression 
Test (Madrid, Spain)  2009;18(3):463-467.
PMCID: PMC2996722  PMID: 21152356

Results 1-4 (4)