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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