|
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
|
