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Including auxiliary variables to assist imputation of substantial amounts of missing data reduces bias.
The presence of missing data reduces the likelihood of identifying true matches.
Excluding cases with missing data reduces the analysable sample size and wastes valuable information that has been collected.
Thus, this study is adequately powered even if a higher than anticipated drop out rate occurs, or if missing data reduces the N for some of the analyses.
It has been demonstrated that imputation of missing data reduces the risk of bias and is preferable over complete case analysis [ 19, 20].
We imputed these data with the mean values of the other patients, as it has been demonstrated that imputation of missing data reduces the risk of bias and is preferable over complete case analysis [ 22, 23].
Similar(54)
For Bone Mineral Density (BMD), missing data reduced the sample size to 74.
A final restriction is the uncertainty about the amount of missing data, as increasing numbers of missing data reduce power.
Depending on the research question, attrition and missing data reduced the sample as shown in Fig. 1.
Missing data reduced sample sizes for fully adjusted models (vs. crude) by 11% for waist circumference and 4.5% for insulin resistance.
Low levels of missing data reduce the likelihood of there being systematic differences between those who complete questions and those who do not in a survey [ 19, 30].
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