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For each article we determined the magnitude of missing data and method(s) for handling missing data in the principal analysis.
Despite the limitations of the present study, i.e. choice of control group, limited generalizability due to the secondary setting, magnitude of missing data and small sample size, this is a hypothesis worth further testing.
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These aspects of the design were assessed by comparing answers for selected sets of questions, assessing the range of responses for questions, asking field experts about the adequacy and plausibility of answers, and inspecting the magnitude and nature of missing data.
In addition to the overall sample size, the magnitude of the p-values can be influenced by the proportion of missing data.
Although the similarity across countries in the patterns of relationships between income quartile location and the explanatory variables is reassuring, comparisons of the magnitudes of the logistic regression coefficients are likely sensitive to differences in the amounts of missing data.
When only patients with available data at all visits were analyzed the same magnitude and pattern of bone loss was seen compared to when data were handled with imputation of missing data as shown in Figure 1.
However, although there was statistical evidence for differences in several characteristics between the eligible sample and those excluded because of missing data on maternal 25(OH D and/or offspring cardiovascular risk factors, these differences were small in magnitude.
When some entries in the training set are missing, methods exist to learn these classifiers under some assumptions about the pattern of missing data.
The rate of missing data was 2.4%.
Since the amount of missing data were low, no assumptions were made for missing data.
Also called the rate of missing information, it differs from the percentage of missing data.
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