General Category of Statistical Method | Specific Method Suggested | Number of Resources1 | Citations |
---|---|---|---|
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 | Â | Â | Â |
1. ANOVA2 analogue (e.g., a categorical moderator) | Â | 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 | Â | 1 | 48 |
4. Multiple univariate analyses with Bonferroni adjustments | Â | 1 | 48 |
5. Meta-analysis of interaction estimates | Â | 1 | 61 |
6. Model to include the repeated observations (time as a variable) using IPD | Â | 1 | 109 |
7. Z test | Â | 1 | 125 |
Bayesian Approaches | Â | Â | |
1. Hierarchical Bayesian modeling | Â | 2 | 44, 48 |
2. Random effects models | Â | 1 | 63 |
Data Specific Approaches | Â | Â | Â |
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 | Â | Â | Â |
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 | Â | 1 | 72 |
Combining regression coefficients from separate studies | Â | 1 | 64 |