diagnostic-meta-analysis

To perform a diagnostic test accuracy meta-analysis, provide a clean 2x2 dataset of TP, FP, FN, and TN values. The analysis aggregates sensitivity and specificity across studies, accounts for threshold effects, and visualizes the combined results using SROC plots.

Bivariate and HSROC models are two approaches to combine diagnostic accuracy data. The bivariate model focuses on pooling sensitivity and specificity jointly, while the HSROC model explicitly models threshold effects to produce summary receiver operating characteristic curves.

You incorporate QUADAS-2 quality assessments by using them to guide bias exploration and quality assessment in your diagnostic test accuracy review. This ensures that variations in study design and risk of bias are accounted for when synthesizing sensitivity and specificity.

Yes, you can use mada and metafor for SROC visualization and modeling in diagnostic meta-analysis. These packages support both bivariate and HSROC modeling approaches to generate summary receiver operating characteristic outputs from 2x2 diagnostic data.

You need diagnostic test accuracy meta-analysis when aggregating paired accuracy metrics like sensitivity and specificity across studies, because standard meta-analysis cannot handle the correlated nature of these outcomes or model threshold effects via SROC curves.