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Simulations were then repeated with missing data generated from the complete datasets.
The type I error rate still remained stable and close to the expected value (5%) for all methods, whatever the proportion of missing data generated (see Table A3 in Additional file 3).
Obesity prevention has been difficult to implement and major impediments include power inequalities across various actors due to lack of engagement. A. Linden provides an informative analysis of the missing data generated as the unintended consequence of lack of patient (or service users) engagement in public health research.
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While the regression was performed using only those participants who had complete data for all the predictors included in the model (n = 95), the path analysis was performed using maximum likelihood method, which dealt with missing data generating a model including the 152 participants.
In both cases, no missing data were generated at baseline.
The missing data is generated solely from the observed audit outcome data.
Because all of the responses were 'forced-choice,' no missing data were generated.
Thus, when missing data were generated, the statistical power of LPCM increased.
Accordingly, in order to minimize any possible effects of missing data, we generated two different data matrices for phylogenetic analyses.
A missing data is generated when the GenCall score for a particular individual and SNP is below 0.25 [ 30].
Simulation studies have demonstrated that if the sites that contain missing data are generated by the same process as complete sites, then their inclusion will not be detrimental to phylogenetic inference (Wiens and Morrill 2011).
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