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Although unconditional LGM is an improvement over techniques that exclude data with missing values, these models must still be interpreted with caution since for all cognitive measures, significant amounts of variance remained unexplained.
Data with missing values (about 0.7%) were excluded for analyzing HIV prevalence.
Data with missing values in any question were excluded (n = 1,981).
To eliminate the influence of missing value replacement, we excluded participants with missing values and performed a statistical analysis of data without replacement.
We also analysed the data after excluding participants with missing values for any covariate.
Missing value imputation: As described above, we excluded variables with missing values more than 20%.
Percentages calculated excluding women with missing values.
The SVM analysis excludes participants with missing values.
c): Excluding 56 observations with missing values.
b): Excluding 32 observations with missing values.
For the analyses on pain characteristics, data sets with missing values were excluded from the corresponding analyses (available case-analysis).
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