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Third missing data affected our analysis.
Moreover, missing data affects study power simply by the reduced availability of data points [45].
If the missing data affects outcome measure, the participant will be rejected.
Thus, while missing data affected the prevalence of metabolic syndrome characteristics it had a negligible effect on the association of EHR metabolic syndrome on adverse outcomes.
However, missing data affected our ability to assess accuracy of parent-report compared to the EHR, especially for the Black and Asian subgroups.
We also examined whether individuals with missing data affected the outcomes and found that, when individuals with missing covariate information (n = 78) were excluded, the estimates were nearly unchanged [i.e., SBP β12, 0.15 (95% CI: 0.02, 0.28)] (data not shown).
We relied on bootstrap support because 1) we are interested in directly assessing how adding characters with missing data affects sampling variance and 2) the 567-taxon data sets appear to have much trouble reaching stationarity in Bayesian analyses [ 3].
Both types of analysis favoured bariatric surgery over non-surgical treatment concerning remission of type 2 diabetes, but the assumptions made about missing data affected the effect size substantially.
With regards to imputation, we wish to examine how imputation of missing data affects RA trials investigating clinical response, as this have, to our knowledge, not previously been carried out.
Most demographic, clinical and service use variables were complete or near complete, but missing data affected some other variables – especially discharge HoNOS scores – and, although we used the best available techniques to accommodate these missing values, the estimates of association between the affected variables and the odds of FD should be viewed cautiously.
The amount of missing data can affect generalizability of the results.
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