Comprehensive meta-analysis of diagnostic test accuracy studies designed for pathology research. Performs bivariate random-effects modeling, HSROC analysis, meta-regression, and publication bias assessment for AI algorithm validation and biomarker diagnostic accuracy synthesis.
Usage
diagnosticmeta(
data,
study,
true_positives,
false_positives,
false_negatives,
true_negatives,
covariate,
bivariate_analysis = TRUE,
hsroc_analysis = FALSE,
meta_regression = FALSE,
heterogeneity_analysis = FALSE,
publication_bias = FALSE,
confidence_level = 95,
method = "reml",
forest_plot = FALSE,
sroc_plot = FALSE,
funnel_plot = FALSE,
show_individual_studies = FALSE,
show_interpretation = FALSE,
show_methodology = FALSE,
show_analysis_summary = FALSE,
color_palette = "standard",
show_plot_explanations = FALSE
)Arguments
- data
the data as a data frame
- study
Variable containing unique study identifiers
- true_positives
Number of true positive results in each study
- false_positives
Number of false positive results in each study
- false_negatives
Number of false negative results in each study
- true_negatives
Number of true negative results in each study
- covariate
Optional covariate for meta-regression analysis
- bivariate_analysis
Perform bivariate random-effects meta-analysis
- hsroc_analysis
Perform hierarchical summary ROC (HSROC) analysis
- meta_regression
Perform meta-regression with specified covariate
- heterogeneity_analysis
Perform heterogeneity analysis including I-squared and Q statistics
- publication_bias
Assess publication bias using Deeks' funnel plot test
- confidence_level
Confidence level for meta-analysis results
- method
Method for meta-analysis estimation
- forest_plot
Generate forest plot for sensitivity and specificity
- sroc_plot
Generate summary receiver operating characteristic plot
- funnel_plot
Generate funnel plot for publication bias assessment
- show_individual_studies
Display results for individual studies in summary tables
- show_interpretation
Display clinical interpretation guidelines and recommendations
- show_methodology
Display detailed methodology and statistical approach information
- show_analysis_summary
Display natural language summary of analysis results
- color_palette
Color palette for all plots - choose color-blind safe options for accessibility
- show_plot_explanations
Display detailed explanations for all plots including interpretation guidance
Value
A results object containing:
results$welcome | a html | ||||
results$instructions | a html | ||||
results$summary | a html | ||||
results$about | a html | ||||
results$bivariateresults | a table | ||||
results$hsrocresults | a table | ||||
results$heterogeneity | a table | ||||
results$metaregression | a table | ||||
results$publicationbias | a table | ||||
results$individualstudies | a table | ||||
results$forestplot | an image | ||||
results$srocplot | an image | ||||
results$funnelplot | an image | ||||
results$interpretation | a html | ||||
results$forestplot_explanation | a html | ||||
results$srocplot_explanation | a html | ||||
results$funnelplot_explanation | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$bivariateresults$asDF
as.data.frame(results$bivariateresults)
Examples
data('diagnostic_studies')
diagnosticmeta(data = diagnostic_studies,
study = study_name,
true_positives = tp,
false_positives = fp,
false_negatives = fn,
true_negatives = tn)