2.4 | Statistical analysis
We used the extracted True positives (TP), True negatives (TN), False
positives (FP), and False negatives (FN) data to calculate the pooled
sensitivity, specificity, positive likelihood ratio (PLR), negative
likelihood ratio (NLR), and diagnostic odds ratio (DOR) based on
bivariate generalized linear mixed modeling(35). We constructed a
summary receiver operating characteristic (SROC) curve. The overall
diagnostic accuracy of RAC for H. pylori was determined by calculating
the area under the SROC curve (AUROC). We calculated the Q-value and
I2 to assess heterogeneity between studies, which can
be quantified as low, moderate, and high, with upper limits of 25%,
50%, and 75% for the I2 statistics,
respectively(36). Meta-regression and subgroup analysis were performed
to identify potential factors that could contribute to heterogeneity
between studies.
We used the Fagan nomogram to estimate a patient’s posttest probability
of being infected with H. pylori based on a pretest probability.
Furthermore, a likelihood ratio scattergram was used to evaluate the
clinical utility of RAC for the diagnosis of H. pylori infection. In
detail, a PLR>10 suggests that the index test can be used
for confirmation of H. pylori infection, and an NLR<0.1
suggests it can be used for the exclusion of H. pylori infection.
Publication bias was assessed using the Deeks funnel plot asymmetry
test. We used the MIDAs module for STATA (version 12, StataCorp LP in
College Station, TX) for the bivariate summary receiver operating curve
analysis. Revman software (version 5.3, Cochrane Collaboration) was used
for quality assessment.