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--- tips [2022/07/21 12:42] – Wolfgang Viechtbauer
+++ tips [2022/09/06 08:19] – Wolfgang Viechtbauer
@@ Line 5: / Line 5: @@
 **Note:** If an example does not work properly, try installing the development version of the metafor package as described [[installation#development_version|here]].
-  * [[tips:handling_missing_data|Handling Missing Data in Output/Figures]]: An illustration/discussion of how to show studies in figures and output that were actually excluded from model fitting due to missing data.
+==== Data Preparation & Management ====
   * [[tips:assembling_data_smd|Assembling Data for a Meta-Analysis of Standardized Mean Differences]]: An illustration of how a dataset for a meta-analysis of standardized mean differences (Cohen's d values) can be assembled/constructed from various pieces of information.
   * [[tips:assembling_data_or|Assembling Data for a Meta-Analysis of (Log) Odds Ratios]]: An illustration of how a dataset for a meta-analysis of (log) odds ratios can be assembled/constructed from various pieces of information.
+==== Comparisons Between Different Functions, Models, and Software ====
   * [[tips:regression_with_rma|Linear Regression and the Mixed-Effects Meta-Regression Model]]: An illustration of the relationship between the linear regression model (fitted by the ''lm()'' function) and the mixed-effects meta-regression model (fitted by the ''rma()'' function).
+  * [[tips:rma.uni_vs_rma.mv|A Comparison of the rma.uni() and rma.mv() Functions]]: A comparison of the ''rma.uni()'' and ''rma.mv()'' functions for fitting equal- and random-effects models.
+  * [[tips:rma_vs_lm_lme_lmer|A Comparison of the rma() and the lm(), lme(), and lmer() Functions]]: An illustration of the difference between the models fitted by the ''rma()'' function and the models fitted by the ''lm()'', ''lme()'', and ''lmer()'' functions.
   * [[tips:two_stage_analysis|Two-Stage Analysis versus Linear Mixed-Effects Models for Longitudinal Data]]: An illustration of two different approaches to analyzing longitudinal data: A two-stage analysis (which the ''rma.mv()'' function can be used for) and linear mixed-effects models (e.g., using the ''lme()'' function).
-  * [[tips:rma.uni_vs_rma.mv|A Comparison of the rma.uni() and rma.mv() Functions]]: A comparison of the ''rma.uni()'' and ''rma.mv()'' functions for fitting equal- and random-effects models.
+  * [[tips:comp_mh_different_software|Comparison of the Mantel-Haenszel Method in Different Software]]: A comparison of the results obtained with the Mantel-Haenszel method as implemented in metafor and other software.
+  * [[tips:hunter_schmidt_method|Hunter and Schmidt Method]]: A discussion of how one can conduct a meta-analysis according to the Hunter & Schmidt method with the metafor package.
+  * [[tips:clogit_paired_binary_data|Conditional Logistic Regression for Paired Binary Data]]: An illustration of how to fit the conditional logistic regression model for paired binary data.
-  * [[tips:rma_vs_lm_lme_lmer|A Comparison of the rma() and the lm(), lme(), and lmer() Functions]]: An illustration of the difference between the models fitted by the ''rma()'' function and the models fitted by the ''lm()'', ''lme()'', and ''lmer()'' functions (or: why the ''lm()'', ''lme()'', and ''lmer()'' functions cannot be used to fit meta-analytic models).
+==== Meta-Regression ====
   * [[tips:models_with_or_without_intercept|Meta-Regression Models With or Without an Intercept]]: A discussion of what happens when we fit meta-regression models with or without an intercept.
+  * [[tips:meta_regression_with_log_rr|Interpreting Coefficients in Meta-Regression Models with (Log) Risk Ratios]]: A little tutorial on how to interpret the coefficients in a meta-regression model when using the log risk ratio as the outcome measure.
   * [[tips:testing_factors_lincoms|Testing Factors and Linear Combinations of Parameters]]: An illustration of how to test factors and linear combinations of parameters in (mixed-effects) meta-regression models.
@@ Line 25: / Line 37: @@
   * [[tips:multiple_factors_interactions|Models with Multiple Factors and Their Interaction]]: An illustration of how to examine and conduct tests of models involving multiple factors and their interaction.
-  * [[tips:meta_regression_with_log_rr|Interpreting Coefficients in Meta-Regression Models with (Log) Risk Ratios]]: A little tutorial on how to interpret the coefficients in a meta-regression model when using the log risk ratio as the outcome measure.
+  * [[tips:computing_adjusted_effects|Computing Adjusted Effects Based on Meta-Regression Models]]: A discussion of how to compute 'adjusted effects' based on meta-regression models.
-  * [[tips:bootstrapping_with_ma|Bootstrapping with Meta-Analytic Models]]: An example showing how to conduct parametric and non-parametric bootstrapping with meta-analytic models.
+  * [[tips:diff_omnibus_vs_individual_tests|Difference Between the Omnibus Test and Tests of Individual Predictors]]: An illustration and discussion of the phenomenon where the result from the 'omnibus test' conflicts with that from tests of the individual predictors in a meta-regression model.
-  * [[tips:comp_mh_different_software|Comparison of the Mantel-Haenszel Method in Different Software]]: A comparison of the results obtained with the Mantel-Haenszel method as implemented in metafor and other software.
+  * [[tips:increasing_tau2_when_adding_moderators|Increasing $\tau^2$ When Adding Moderators]]: An illustration of the somewhat counterintuitive phenomenon of an increasing estimate of $\tau^2$ when adding moderators in a meta-regression model.
   * [[tips:comp_two_independent_estimates|Comparing Estimates of Independent Meta-Analyses or Subgroups]]: An illustration of how to compare two estimates from two independent meta-analyses or subgroups of studies.
-  * [[tips:model_selection_with_glmulti_and_mumin|Model Selection using the glmulti and MuMIn Packages]]: An illustration of how to use the metafor package in combination with the glmulti and MuMIn packages for model selection and multimodel inference based on an information-theoretic approach.
+  * [[tips:non_linear_meta_regression|Modeling Non-Linear Associations in Meta-Regression]]: An illustration of how to model non-linear associations in meta-regression using polynomial and restricted cubic spline models.
-  * [[tips:convergence_problems_rma|Convergence Problems with the rma() Function]]: A discussion and illustration of convergence problems that can rise when fitting random/mixed-effects (meta-regression) models with the ''rma()'' function.
+==== Package Interoperability ====
-  * [[tips:clogit_paired_binary_data|Conditional Logistic Regression for Paired Binary Data]]: An illustration of how to fit the conditional logistic regression model for paired binary data.
+  * [[tips:bootstrapping_with_ma|Bootstrapping with Meta-Analytic Models]]: An example showing how to conduct parametric and non-parametric bootstrapping with meta-analytic models using the boot package.
-  * [[tips:i2_multilevel_multivariate|$I^2$ for Multilevel and Multivariate Models]]: A discussion of how one can compute $I^2$-type statistics in multilevel and multivariate models.
+  * [[tips:model_selection_with_glmulti_and_mumin|Model Selection using the glmulti and MuMIn Packages]]: An illustration of how to use the metafor package in combination with the glmulti and MuMIn packages for model selection and multimodel inference based on an information-theoretic approach.
-  * [[tips:hunter_schmidt_method|Hunter and Schmidt Method]]: A discussion of how one can conduct meta-analyses according to the Hunter & Schmidt method.
+  * [[tips:multiple_imputation_with_mice_and_metafor|Multiple Imputation with the mice and metafor Packages]]: An illustration of how to do multiple imputation together with the mice and metafor packages.
-  * [[tips:speeding_up_model_fitting|Speeding Up Model Fitting]]: A discussion of some methods and strategies for speeding up model fitting with complex models.
+==== Plots and Figures ====
-  * [[tips:multiple_imputation_with_mice_and_metafor|Multiple Imputation with the mice and metafor Packages]]: An illustration of how to do multiple imputation together with the mice and metafor packages.
+  * [[tips:handling_missing_data|Handling Missing Data in Output/Figures]]: An illustration/discussion of how to show studies in figures and output that were actually excluded from model fitting due to missing data.
-  * [[tips:non_linear_meta_regression|Modeling Non-Linear Associations in Meta-Regression]]: An illustration of how to model non-linear associations in meta-regression using polynomial and restricted cubic spline models.
+  * [[tips:forest_plot_with_exact_cis|Forest Plot with Exact Confidence Intervals]]: An illustration of how to create a forest plot that shows 'exact' confidence intervals for the observed outcomes of the individual studies.
-  * [[tips:computing_adjusted_effects|Computing Adjusted Effects Based on Meta-Regression Models]]: A discussion of how to compute 'adjusted effects' based on meta-regression models.
+  * [[tips:forest_plot_with_aggregated_values|Forest Plot with Aggregated Values]]: An illustration of how to create a forest plot that shows aggregated estimates for studies that contribute multiple estimates to the analysis.
-  * [[tips:weights_in_rma.mv_models|Weights in Models Fitted with the rma.mv() Function]]: A discussion of how weighting works in more complex models, such as those that can be fitted with the ''rma.mv()'' function.
+==== Multilevel/Multivariate Models ====
-  * [[tips:input_to_rma_function|Specifying Inputs to the rma() Function]]: A discussion of how the inputs to the ''rma()'' function should be specified (and how, on occasion, they have been incorrectly specified).
+  * [[tips:i2_multilevel_multivariate|$I^2$ for Multilevel and Multivariate Models]]: A discussion of how one can compute $I^2$-type statistics in multilevel and multivariate models.
-  * [[tips:forest_plot_with_exact_cis|Forest Plot with Exact Confidence Intervals]]: An illustration of how to create a forest plot that shows 'exact' confidence intervals for the observed outcomes of the individual studies.
+  * [[tips:weights_in_rma.mv_models|Weights in Models Fitted with the rma.mv() Function]]: A discussion of how weighting works in more complex models, such as those that can be fitted with the ''rma.mv()'' function.
-  * [[tips:forest_plot_with_aggregated_values|Forest Plot with Aggregated Values]]: An illustration of how to create a forest plot that shows aggregated estimates for studies that contribute multiple estimates to the analysis.
+==== Miscellaneous Topics ====
-  * [[tips:increasing_tau2_when_adding_moderators|Increasing $\tau^2$ When Adding Moderators]]: An illustration of the somewhat counterintuitive phenomenon of an increasing estimate of $\tau^2$ when adding moderators in a meta-regression model.
+  * [[tips:input_to_rma_function|Specifying Inputs to the rma() Function]]: A discussion of how the inputs to the ''rma()'' function should be specified (and how, on occasion, they have been incorrectly specified).
-  * [[tips:diff_omnibus_vs_individual_tests|Difference Between the Omnibus Test and Tests of Individual Predictors]]: An illustration and discussion of the phenomenon where the result from the 'omnibus test' conflicts with that from tests of the individual predictors in a meta-regression model.
+  * [[tips:speeding_up_model_fitting|Speeding Up Model Fitting]]: A discussion of some methods and strategies for speeding up model fitting with complex models.
+  * [[tips:convergence_problems_rma|Convergence Problems with the rma() Function]]: A discussion and illustration of convergence problems that can rise when fitting random/mixed-effects (meta-regression) models with the ''rma()'' function.