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. 2019:41:e2019007.
doi: 10.4178/epih.e2019007. Epub 2019 Mar 28.

Diagnostic test accuracy: application and practice using R software

Sung Ryul Shim  1   2 , Seong-Jang Kim  3   4 , Jonghoo Lee  5
Affiliations

Diagnostic test accuracy: application and practice using R software

Sung Ryul Shim et al. Epidemiol Health. 2019.

Abstract

The objective of this paper is to describe general approaches of diagnostic test accuracy (DTA) that are available for the quantitative synthesis of data using R software. We conduct a DTA that summarizes statistics for univariate analysis and bivariate analysis. The package commands of R software were "metaprop" and "metabin" for sensitivity, specificity, and diagnostic odds ratio; forest for forest plot; reitsma of "mada" for a summarized receiver-operating characteristic (ROC) curve; and "metareg" for meta-regression analysis. The estimated total effect sizes, test for heterogeneity and moderator effect, and a summarized ROC curve are reported using R software. In particular, we focus on how to calculate the effect sizes of target studies in DTA. This study focuses on the practical methods of DTA rather than theoretical concepts for researchers whose fields of study were 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; Likelihood ratios; Mada; Meta-analysis; Receiver-operating characteristic curve; Reitsma.

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Conflict of interest statement

The authors have no conflicts of interest to declare for this study.

Figures

Figure 1.
Figure 1.
Summary statistics for diagnostic test accuracy.
Figure 2.
Figure 2.
Flow chart of diagnostic test accuracy (DTA) using R “mada” & “meta” package. TP, true positive; FP, false positive; FN, false negative; TN, true negative; DOR, diagnostic odds ratio; SROC, summary receiver operating characteristic.
Figure 3.
Figure 3.
Univariate analysis: sensitivity. CI, confidence interval; g, subgroup.
Figure 4.
Figure 4.
Univariate analysis: diagnostic odds ratio. OR, odds ratio; CI, confidence interval; g, subgroup.
Figure 5.
Figure 5.
Summary receiver operating characteristic (SROC) curve (bivariate model) for diagnostic test accuracy. CI, confidence interval; AUC, area under the curve; DOR, diagnostic odds ratio.
See this image and copyright information in PMC

References

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