Comprehensive meta-analysis and evidence synthesis including forest plots, heterogeneity testing, publication bias assessment, diagnostic test accuracy meta-analysis, and network meta-analysis.
Usage
metaanalysis(
data,
effect_size,
variance,
study_id,
sample_size,
year,
analysis_type = "generic",
model_type = "random_effects",
effect_measure = "odds_ratio",
heterogeneity_method = "dersimonian_laird",
true_positives,
false_positives,
false_negatives,
true_negatives,
dta_model_type = "bivariate",
treatment_arm,
comparison_arm,
network_method = "frequentist",
publication_bias = TRUE,
bias_tests = "all_tests",
subgroup_var,
moderator_vars,
meta_regression = FALSE,
sensitivity_analysis = TRUE,
outlier_detection = TRUE,
forest_plot_options = TRUE,
prediction_interval = TRUE,
confidence_level = 0.95,
robust_methods = FALSE,
small_sample_correction = TRUE,
knha_adjustment = TRUE
)Arguments
- data
the data as a data frame
- effect_size
Effect size variable (e.g., log odds ratio, Cohen's d, log hazard ratio)
- variance
Variance or standard error of the effect size
- study_id
Study identifier for each effect size (required for meta-analysis)
- sample_size
Sample size for each study (optional, used for sensitivity analysis)
- year
Publication year for temporal trend analysis and publication bias assessment
- analysis_type
Type of meta-analysis to perform: Standard for combining effect sizes, Diagnostic for test accuracy studies, Network for multiple treatment comparisons
- model_type
Random-effects is usually preferred as it accounts for differences between studies
- effect_measure
Choose based on your outcome type: continuous (mean difference), binary (odds/risk ratio), time-to-event (hazard ratio), or association (correlation)
- heterogeneity_method
Method for estimating between-study heterogeneity (tau-squared)
- true_positives
True positives for diagnostic test accuracy meta-analysis
- false_positives
False positives for diagnostic test accuracy meta-analysis
- false_negatives
False negatives for diagnostic test accuracy meta-analysis
- true_negatives
True negatives for diagnostic test accuracy meta-analysis
- dta_model_type
Model type for diagnostic test accuracy meta-analysis
- treatment_arm
Treatment arm identifier for network meta-analysis
- comparison_arm
Comparison arm identifier for network meta-analysis
- network_method
Statistical approach for network meta-analysis
- publication_bias
Perform publication bias assessment using funnel plots and statistical tests
- bias_tests
Statistical tests for publication bias assessment
- subgroup_var
Categorical variable for subgroup analysis
- moderator_vars
Variables for meta-regression analysis
- meta_regression
Perform meta-regression analysis with moderator variables
- sensitivity_analysis
Perform sensitivity analysis including leave-one-out and influence diagnostics
- outlier_detection
Detect outlying studies using standardized residuals and influence measures
- forest_plot_options
Enable forest plot customization options
- prediction_interval
Include prediction intervals in forest plots and results
- confidence_level
Confidence level for confidence and prediction intervals
- robust_methods
Use robust methods for outlier-resistant meta-analysis
- small_sample_correction
Apply small sample corrections (Hartung-Knapp adjustment)
- knha_adjustment
Apply Knapp-Hartung adjustment for random-effects models
Value
A results object containing:
results$instructions | a html | ||||
results$studySummary | a table | ||||
results$overallResults | a table | ||||
results$heterogeneityAssessment | a table | ||||
results$publicationBiasResults | a table | ||||
results$subgroupAnalysis | a table | ||||
results$metaRegressionResults | a table | ||||
results$diagnosticAccuracyResults | a table | ||||
results$networkResults | a table | ||||
results$sensitivityAnalysis | a table | ||||
results$outlierAnalysis | a table | ||||
results$modelFitStatistics | a table | ||||
results$forestPlot | an image | ||||
results$funnelPlot | an image | ||||
results$heterogeneityPlot | an image | ||||
results$metaRegressionPlot | an image | ||||
results$srocPlot | an image | ||||
results$networkPlot | an image | ||||
results$methodExplanation | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$studySummary$asDF
as.data.frame(results$studySummary)