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In another point of view when considering additional data with missing values, this would wrongly increase the index value.
In this method, the last observed values for a patient are used in place of the missing values; this method assumes that the last known state in the study represents the patient's true outcome [ 2].
Because each model contained different prognostic factors with possibly missing values, this resulted in three different 'general' cohorts, as patients with missing predictor values were excluded from external validation.
Although there are other strategies for imputing missing values, this computation is consistent with the previous PedsQL™ peer-reviewed publications, as well as other well-established HRQOL measures [ 25, 29, 30].
Although there are other strategies for imputing missing values, this computation is consistent with the previous PedsQL™ peer-reviewed publications, as well as other well-established HRQOL measures [ 37, 40, 41].
Although there are other strategies for imputing missing values, this computation is consistent with the previous PedsQL™ peer-reviewed publications, as well as other well-established HRQOL measures [ 23, 24].
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However, considering less than 1 % data were excluded owing to the missing value, this problem may not be serious.
Because in CGSS2008 there are too many missing values to this question, this study uses another measurement as a substitute.
A fruitful replacement of missing values for this purpose is done by means of a static PCA model.
Due to missing values in this variable, the sample size is reduced to 17,989 observations.
The first item above can be seen as avoiding missing values within this approach (third subindex) concerning an index.
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