Marginal regression for repeated binary data with outcome subject to non-ignorable non-response
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Published on: December 9, 2015
1Biometry Branch, National Cancer Institute, Bethesda, Maryland 20892-7354, USA.
Abstract:
Using a model that accounts for non-ignorable non-response, we analyzed data from the Muscatine Risk Factor Study (Woolson and Clarke, 1984, Journal of the Royal Statistical Society, Series A 147, 87-99) on the effects of gender and age on obesity in schoolchildren. The methodology is related to that of Diggle and Kenward (1994, Applied Statistics 43, 49-93), except that the repeated data are binary, not continuous, and the non-response occurs in various patterns, not just dropouts. We found strong evidence that non-response was non-ignorable. In addition, we found that the proportion of children who were obese differed significantly with gender and increased with age.
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