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In some of the descriptive statistics there are occasionally missing values due to missing information, these missing values can be regarded as random.
For the ITT analysis, missing values due to loss of respondents or missing item scores will be handled through multiple imputation using the Markov Chain Monte Carlo method in the statistical package Mplus6 [ 44].
For patients entering the OLE, missing values during the OLE due to patient withdrawal (AE or lack of efficacy) or data exclusion after rescue medication use were imputed as non-remitters, whereas missing values due to other withdrawal reasons, study completion, or a missing assessment were imputed by carrying forward the last available post-RCT entry score.
In order to assist machine learning algorithms differentiate missing values due to non-applicability and missing values due to survey issues, multi-part questions that disqualify future answers are typically converted to a 0 or −1 (depending the context).
In the current dataset, 2.8 % of the data were set to missing values due to low gaze confidence values.
Any missing values due to loss of tumor tissue on cell blocks or if IHC was not performed were eliminated from the statistical analysis.
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Only 0.5% of missing values were due to the food composition database; all the remaining missing values were due to lack of information in commercial food labels.
Three missing values; one due to missing identification and the second and third due to non-complete sets of radiographs.
*- Missing value due to no response by participants The coping strategies adopted by physiotherapists with WRMDs in Nigeria are shown in Table 5.
In the validation cohort for the Teramukai model, 67% of missing values was due to the absence of performance status.
Missing values were due to removal of the portable recording device < 2 h after waking up (156 calculations) and insufficient night-measurements (27 calculations).
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