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Our approach is mainly focused on regression models and in the future we plan to extend it to classification models, including the analysis of model variables in chemical space partitioning.
Participant characteristics and distributions of model variables in categorical items are presented in Table 1.
Monthly income had significant influence on all model variables in our participants.
Note: The table reports the distribution of the model variables in the case and control groups for age groups 0-19 years.
AOR-Adjusted odds ratio; CI-Confidence interval; '-' variable not significant in final model; Variables in the multivariable model are adjusted for all other variables in the model.
In addition, marital status, occupation, CD4 cell count, and the route of HIV infection were not related to any model variables in this study.
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P values of 0.05 were chosen as significance levels for gene selection after Benjamini-Hochberg correction and for model variable in maSigPro.
For scoring the different models, variables in the model were weighted by their respective logistic regression coefficient.
Descriptive statistics for model variables appear in Table 1.
To the final model variables identified in the first steps were entered together.
Model 3: variables in Model 1 plus all area level factors (urbanization, CDI, medical service supply).
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