Table 4 Statistical suggestions for investigating aspects of clinical heterogeneity

Subgroup analyses

General

18

60, 2324, 25,46, 48, 50, 75, 92, 94, 93, 27, 97, 100, 98, 115, 105, 19

Hierarchical testing procedure based on the heterogeneity statistic Q

1

114

Combining subgroups across studies (i.e., in stratified studies)

1

114

Moderator Analyses

1. ANOVA² analogue (e.g., a categorical moderator)

4

48, 94, 95, 114

2. Meta-regression

General mention

16

19, 60, 6, 24, 2528, 31,32,43, 50, 75, 94, 95, 100, 98, 93, 1325, 418

Fixed effects model (general)

4

92, 93, 94, 95

Bayesian models (general)

4

66, 71, 124, 95

New maximum likelihood method

2

60, 124

New weighted least squares model

2

58, 67

Random effects model (general)

2

67, 114

Random effects model for IPD³

2

58, 61

Permutation-based resampling

2

31, 43

Other nonparametric (e.g., fractional polynomials, splines)

2

69, 85

Mixed effects model

2

38, 114

New variance estimators (for covariates)

2

77, 84

Methods for measurement of residual errors

2

59, 41

Bayesian model in the presence of missing study-level covariate data

1

110

Semi-parametric modeling (general)

1

80

Fixed effects generalized least squares model

1

68

Hierarchical regression models

3

60, 64, 124

Random effects model with new variance estimator

1

70

Logistic regression with binary outcomes

1

25

Interaction term for meta-regression model

1

95

Consider nonlinear relationships (e.g., use quadratic or log transformations)

1

48

Bayesian model for use in meta-analyses of multiple treatment comparisons

1

111

3. Multivariate analyses

1

48

4. Multiple univariate analyses with Bonferroni adjustments

1

48

5. Meta-analysis of interaction estimates

1

61

6. Model to include the repeated observations (time as a variable) using IPD

1

109

7. Z test

1

125

Bayesian Approaches

1. Hierarchical Bayesian modeling

2

44, 48

2. Random effects models

1

63

Data Specific Approaches

1. IPD analyses

General

5

75, 76, 95, 97, 23

Regression

1

61, 46

Adding a treatment-covariate interaction term

1

95

2. Combination of IPD & APD⁴

Two-step models

2

74, 78

Multi-level model

2

69, 100

Meta-analysis of interaction estimates

1

61

Other Approaches

Models for control event rate / baseline risk

General (e.g., control event rate)

10

63, 24, 71, 81, 79, 93, 100, 19, 78, 111

Structural equation modeling (SEM)

Integration of SEM with fixed, random and mixed effects meta-analyses

1

42

Mixed treatment comparisons combined with meta-regression

1

72

Combining regression coefficients from separate studies

1

64