Team

Distribution

Treatment of nonindependence

Number of covariates

Analytics Approach

OR

1

Linear

Clustered standard errors

7

Ordinary least squares regression with robust standard errors, logistic regression

1.18 [0.95, 1.41]

6

Linear

Clustered standard errors

6

Linear probability model

1.28 [0.77, 2.13]

14

Linear

Clustered standard errors

6

Weighted least squares regression with clustered standard errors

1.21 [0.97, 1.46]

4

Linear

None

3

Spearman correlation

1.21 [1.20, 1.21]

11

Linear

None

4

Multiple linear regression

1.25 [1.05, 1.49]

10

Linear

Variance component

3

Multilevel regression and logistic regression

1.03 [1.01. 1.05]

2

Logistic

Clustered standard errors

6

Linear probability model, logistic regression

1.34 [1.10, 1.63]

30

Logistic

Clustered standard errors

3

Clustered robust binomial logistic regression

1.28 [1.04, 1.57]

31

Logistic

Clustered standard errors

6

Logistic regression

1.12 [0.88, 1.43]

32

Logistic

Clustered standard errors

1

Generalized linear models for binary data

1.39 [1.10, 1.75]

8

Logistic

None

0

Negative binomial regression with a log link

1.39 [1.17, 1.65]

15

Logistic

None

1

Hierarchical log-linear modeling

1.02 [1.00, 1.03]

3

Logistic

Variance component

2

Multilevel logistic regression using Bayesian inference

1.31 [1.09, 1.57]

5

Logistic

Variance component

0

Generalized linear mixed models

1.38 [1.10, 1.75]

9

Logistic

Variance component

2

Generalized linear mixed-effects models with logit link

1.48 [1.20, 1.84]

17

Logistic

Variance component

2

Bayesian logistic regression

0.96 [0.77, 1.18]

18

Logistic

Variance component

2

Hierarchical Bayes model

1.10 [0.98, 1.27]

23

Logistic

Variance component

2

Mixed-model logistic regression

1.31 [1.10, 1.56]

24

Logistic

Variance component

3

Multilevel logistic regression

1.38 [1.11, 1.72]

25

Logistic

Variance component

4

Multilevel logistic binomial regression

1.42 [1.19, 1,71]

28

Logistic

Variance component

2

Mixed-effects logistic regression

1.38 [1.12, 1.71]

21

Miscellaneous

Clustered standard errors

3

Tobit regression

2.88 [1.03, 11.47]

7

Miscellaneous

None

0

Dirichlet-process Bayesian clustering

1.71 [1.70, 1.72]

12

Poisson

Fixed effect

2

Zero-inflated Poisson regression

0.89 [0.49, 1.60]

27

Poisson

None

1

Poisson regression

2.93 [0.11, 78.66]

13

Poisson

Variance component

1

Poisson multilevel modeling

1.41 [1.13, 1.75]

16

Poisson

Variance component

2

Hierarchical Poisson regression

1.32 [1.06, 1.63]

20

Poisson

Variance component

1

Cross-classified multilevel negative binomial model

1.40 [1.15, 1.71]

26

Poisson

Variance component

6

Hierarchical generalized linear modeling with Poisson samping

1.30 [1.08, 1.56]