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In addressing this issue, most studies use complete case analysis, which excludes cases with missing data, thus potentially introducing selection bias.
As the iBT reading and writing scores were self-reported data with some missing data, thus, error terms for these two variables were correlated.
Therefore, the maximum amount of known data was used to impute missing data, thus reducing imputation error.
This method allows some missing data; thus, all the patients included could still remain in the study.
Our analyses used a missing-indicator approach to deal with missing data; thus, all patients were included in the analyses.
We have used model induction algorithms that employ two different strategies of dealing with missing data, thus minimizing the effect.
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Of 74 subjects, 13 patients and 2 healthy subjects had missing information thus data of 59 participants (29 patients in depression group and 30 healthy volunteers in controls) were used.
The sequential imputation uses samples and associated weights to approximate the unknown distribution in the presence of missing data, and thus can be seen as a combination of Gibbs sampler and sequential importance sampling.
Sample attrition and missing data could thus lead to biased results.
Forty-six questionnaires had more than 20% missing data and thus were excluded from the study.
Fifth, our statistical procedures require no missing data, and thus, the small numbers of students who missed a session were excluded from analyses.
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