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Separate analyses were performed on continuous dependent variables (NPRS, ODI and NLARS) for (a) all non-missing values and (b) the 'last observed value carried over' method for missing data.
The data set is analyzed using a hybrid and novel k-means cluster centroid-based method for missing value imputation and C4.5, NB Tree and multilayer perceptron for modeling to predict CAD patients.
We used the missing indicator method for missing data.
All studies reported using the last observation carried forward method for missing observations.
Missing data will be replaced by an imputation method for missing measurements.
An intention-to-treat analysis will be applied using the imputation method for missing data.
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If necessary, multiple imputation or Bayesian methods for missing data will be used as appropriate to address any missing data.
We explored imputation methods for missing scores for this instrument.
Luengo, J., García, S. & Herrera, F. On the choice of the best imputation methods for missing values considering three groups of classification methods.
Authors propose probabilistic models because likelihood based methods for missing data calculation are able to take into account different parameters and time aspect in a single model to convey more trustworthy estimates in performance measurement systems than traditional methods.
Most statistical methods for missing data require some assumptions on missingness mechanisms.
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