Meta-analysis of diagnostic test accuracy studies with multiple thresholds for data integration

Abstract

Objectives: The objective of this study is to introduce methods to use all of the information without omission when individual studies provide multiple effect sizes according to multiple cut-off values (thresholds) during diagnostic test accuracy (DTA) for data integration. For diagnostic test meta-analysis, a general performance method for synthesizing data according to one cut value in one study and a performance method for synthesizing data according to two or more cut values in one study were compared and analyzed.

Methods: As sample data for meta-analysis of DTA studies, 13 DTA studies on prostate cancer (34 effect sizes including total cut-offs) were collected. The summary statistics were calculated and the summary line was analyzed using the "meta", "mada", and "diagmeta" packagesof the R software.

Results: The summary statistics of the random effect model univariate analysis of the "meta" package with a single cut-off corresponding to the highest Youden index in a single study and those of the bivariate analysis of the "mada" package were highly similar. However, in the bivariate analysis of the "diagmeta" package including all cut-off values, the sensitivity decreased and the specificity increased as the amount of data increased.

Conclusions: Considering the heterogeneity of the summary receiver op erating characteristic curve and the use of all given cut-offs, the use of the bivariate analysis model of the "diagmeta" package is recommended. This study focused on practical methods of DTA rather than theoretical concepts for use by researchers whose fields of study are non-statistics related. By performing this study, we hope that many researchers will use R software to determine the DTA more easily, and that there will be greater interest in related research.

Keywords: Diagnostic test accuracy; Diagnostic tests; Evidence based medicine; Meta-analysis; Systematic review; Threshold.

Figure 1.

Univariate analysis using meta package.

Figure 2.

Bivariate analysis using one cut-off within a study (mada package).

Figure 3.

Summary receiver operating characteristic (SROC) curve (bivariate analysis using one cut-off within a study) for diagnostic test accuracy. CI, confidence interval; AUC, area under the curve.

Figure 4.

Bivariate analysis using multiple cut-offs within a study (diagmeta package).

Figure 5.

Summary receiver operating characteristic (SROC) curve (bivariate analysis using multiple cut-offs within a study) for diagnostic test accuracy.