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Table 3 Outcome prevalence estimates using various estimators across populations

From: Unweighted regression models perform better than weighted regression techniques for respondent-driven sampling data: results from a simulation study

Homophily: Outcome prevalence 10% Outcome prevalence 30% Outcome prevalence 50%
1.00 1.10 1.25 1.50 1.00 1.10 1.25 1.50 1.00 1.10 1.25 1.50
Mean outcome prevalence
 naïve 0.09 0.09 0.09 0.09 0.27 0.27 0.27 0.27 0.47 0.47 0.47 0.46
 RDS-I 0.08 0.08 0.08 0.08 0.27 0.26 0.26 0.26 0.47 0.47 0.46 0.46
 RDS-II 0.08 0.08 0.08 0.08 0.27 0.26 0.26 0.26 0.47 0.47 0.46 0.46
 surveylogistic models
  unweighted 0.09 0.09 0.09 0.09 0.27 0.27 0.27 0.27 0.47 0.47 0.47 0.46
  weighted (RDS-II) 0.08 0.08 0.08 0.08 0.27 0.26 0.26 0.26 0.47 0.46 0.46 0.45
Mean SD of outcome prevalence
 naive 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.03 0.02 0.02 0.03 0.03
 RDS-I 0.02 0.02 0.02 0.03 0.04 0.04 0.04 0.04 0.04 0.05 0.05 0.05
 RDS-II 0.02 0.02 0.02 0.03 0.04 0.04 0.04 0.05 0.04 0.05 0.05 0.05
 surveylogistic models
  unweighted 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.03 0.02 0.02 0.03 0.03
  weighted (RDS-II) 0.02 0.02 0.02 0.03 0.04 0.04 0.04 0.04 0.04 0.05 0.05 0.05
Estimator coverage rates
 naive 0.845 0.827 0.802 0.708 0.646 0.740 0.620 0.642 0.742 0.687 0.634 0.551
 RDS-I 0.545 0.554 0.548 0.578 0.572 0.512 0.524 0.501 0.627 0.610 0.569 0.511
 RDS-II 0.772 0.776 0.766 0.749 0.799 0.761 0.744 0.723 0.839 0.831 0.791 0.741
 surveylogistic models
  unweighted 0.916 0.900 0.875 0.784 0.657 0.745 0.611 0.645 0.747 0.684 0.644 0.544
  weighted (RDS-II) 0.828 0.819 0.799 0.769 0.825 0.779 0.778 0.753 0.862 0.835 0.819 0.756