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The missing data resulted in a lower n than the actual number of patients.
Using MAPMAKER with censored BI-markers as missing data resulted in a map that was 38% larger in size than the one produced from two subsets per chromosome with JOINMAP.
For many children, missing data resulted from blood samples not being taken for ESR measurement.
In general, missing data resulted in wider credible intervals due to greater uncertainty around the estimates.
It is clear that missing data resulted in lower prevalences of metabolic syndrome characteristics in the classification that used patients with only three or more criteria measured.
Secondly, for the initial modeling with 52 items, list wise deletion of cases with missing data resulted in a small sample, N = 33.
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Raw signals were filtered using adaptive filters (least mean squares), and processed online to account for missing data resulting from occlusions (interpolation from neighboring markers).
In order to maximize the number of patients in our correlations we used pairwise deletion of missing data resulting in variable numbers of patients ranging from 12 to 17 on which correlations were based.
Missing data resulting from nonresponse occurred for several variables.
First, there is a fraction of students with missing data resulting in low data quality.
As with most survey sampling designs, missing data resulting from non-response occurred in several variables.
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