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Do the available missing data replacement procedures preserve inferential integrity?
The results remained unchanged after using two methods of missing data replacement, the maximum bias method, and the mean value of the group.
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(iii) If, according to the methodology of Yuan and Little (39), the data were not missing at random, the plan was to impute the missing data with replacement values and treat these as if observed.
Thus, (vi - wi) is not computed if vi and/or wi is a missing value To assess the influence of missing data rates and different replacement methods into clustering results (see Figure 1), we have analysed the co-associated genes of an original dataset (without MVs) compared to these genes location in a set with MVs.
The dataset had 5% missing data with no consecutive replacement of missing values (Heck et al. 2010).
Existing service selection models employ random assignment of weights to the QoS attributes, replacement of missing data by random values, etc. which results in an inaccurate ranking of the CSPs.
Due to a combination of the metabolic pathway dynamism, complex sample molecular interactions and overall efficiency of the analytical protocol, the replacement of missing data by means of a multivariate algorithm is preferred to a naive imputation by a pre-specified value like, for instance, zero.
No replacement of missing data took place.
Analyses were performed on all available data, with no replacement of missing data.
No replacement of missing data was planned as part of the analysis of the primary endpoint.
With CLASSIF1 no replacement of missing data values and no mathematical assumptions on parameter distributions are required.
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