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Diagnostic test accuracy: application and practice using R software

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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.

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.

참고문헌 (11)

  1. Hwang SD, "Meta-analysis: from forest plot to network meta-analysis", pp.224~246 (2018), Hannarae
  2. Shim SR, "Diagnostic test accuracy using R & Meta-DiSc software" (2019), SDB Lab
  3. 심성률, "STATA를 이용한 진단검사 정확도 메타분석", 보건정보통계학회지, 40(3), pp.190~199 (2015), 한국보건정보통계학회
  4. Moses LE, "Combining independent studies of a diagnostic test into a summary ROC curve : data analytic approaches and some additional considerations", Stat Med, 12, pp.1293~1316 (1993)
  5. Littenberg B, "Estimating diagnostic accuracy from multiple conflicting reports : a new meta analytic method", Med Decis Making, 13, pp.313~321 (1993)
  6. Reitsma JB, "Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews", J Clin Epidemiol, 58, pp.982~990 (2005)
  7. Rutter CM, "A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations", Stat Med, 20, pp.2865~2884 (2001)
  8. Arends LR, "Bivariate random effects meta-analysis of ROC curves", Med Decis Making, 28, pp.621~638 (2008)
  9. Harbord RM, "A unification of models for meta-analysis of diagnostic accuracy studies", Biostatistics, 8, pp.239~251 (2007)
  10. "mada: meta-analysis of diagnostic accuracy"[URL]
  11. Wu HY, "Diagnostic performance of random urine samples using albumin concentration vs ratio of albumin to creatinine for microalbuminuria screening in patients with diabetes mellitus : a systematic review and meta-analysis", JAMA Intern Med, 174, pp.1108~1115 (2014)

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