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However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply.
Overall, such clinical classification tasks can be considered to be among the hardest; most of the well-known theoretical issues such as high-dimensional feature spaces, few examples, noise and missing data apply [Kaminski and Bar-Joseph, 2007; Lottaz et al., 2008; see Hastie et al. (2001) for a general treatment].
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Once the study is completed, it is essential to assess the pattern of missing data and apply the appropriate statistical analysis.
We will explore sensitivity analyses for missing data by applying worst and best case scenarios (that is, missing data are assumed either as responder or non-responder in the corresponding sensitivity analysis).
Therefore, no imputation technique for missing data was applied.
To handle missing data, we applied the method of multiple imputation [ 26].
However, this could potentially be avoided for binary variables with missing data by applying bootstrapping in the imputation process.
An observed-cases approach to missing data was applied (i.e., a patient's missing values were not imputed or carried forward).
Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied.
Figure 6 provides the estimates of the model performance measures for the different missing data methods applied to increasing levels of MAR missingness.
The total number of polymorphic SNPs without regard to the amount of missing data after applying read-depth filtering amounted to 68,972 for PV and 77,610 for Me.
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