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Table 2 Summary of regression model performance across all populations

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

  Model Weight Clusters Ψ SE Adj. Error Coverage Bias (mean %) Bias (median %) Accuracy (%)
Logistic Regression
 Generalised Linear Models
  glm(R) 1     0.04 0.954 2.07 −1.63 88.1
2 RDS-II     0.55 0.442 20.89 8.51  
3 R-y    0.04 0.955 3.35 −0.48 88.6
4 RDS-II R-y    0.55 0.443 25.56 11.57  
  surveylogistic (SAS) 5     0.05 0.952 2.07 −1.63 88.1
6 RDS-II     0.07 0.903 20.88 8.51  
7    Morel 0.05 0.953 2.07 −1.63 88.1
8 RDS-II    Morel 0.07 0.904 20.88 8.51  
9 RDS-II RwS    0.07 0.903 20.88 8.51  
10 RDS-II RwS   Morel 0.07 0.904 20.88 8.51  
 Generalised Linear Mixed Models
  glmer(R) 11 S U   0.05 0.954 3.48 −0.46 88.1
12 RDS-II S U   0.55 0.402 44.55 26.73  
  glimmix (SAS) 13 S AR   0.04 0.955 3.45 −0.34 88.1
  glimmix (SAS) 14 R CS   0.04 0.957 2.4 −1.19 88.1
  glmmPQL(R) 15 S DC 0.04 0.865 −0.86 −6.34  
 Generalised Estimating Equations
  geeglm(R) 16 R I Classical 0.13 0.952 2.07 −1.63  
17 RDS-II R I Classical 0.16 0.902 20.89 8.51  
  glimmix (SAS) 18 S AR   0.04 0.939 1.85 −1.69  
19 R CS   0.04 0.937 2.52 −1.75  
20 R CS Classical 0.05 0.948 2.52 −1.75  
21 R CS FIRORES 0.05 0.950 2.52 −1.75 88.1
22 R CS FIROEEQ 0.05 0.951 2.52 −1.75 88.1
23 R CS MBN 0.05 0.950 2.52 −1.75  
Poisson Regression
 Generalised Linear Models
  glm(R) 24     0.02 0.962 4.81 4.15 86
  glm(R) 25 RDS-II     0.49 0.457 9.48 8.23  
  glm(R) 26 R-y    0.02 0.964 3.06 2.44 86.3
  glm(R) 27 RDS-II R-y    0.47 0.493 7.74 6.46  
 Generalised Linear Mixed Models
  glmer(R) 28 S U   0.02 0.963 4.92 4.27 86
29 RDS-II S U   0.47 0.431 11.71 10.42  
 Generalised Estimating Equations
  geeglm(R) 30 R I Classical 0.13 0.859 4.81 4.15  
31 RDS-II R I Classical 0.17 0.781 9.48 8.23  
  1. R-y recruiter outcome as covariate, S Seeds, R recruiter, RwS recruiter within seed