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Sung Ryul Shim 5 Articles
Meta-analysis of diagnostic test accuracy studies with multiple thresholds for data integration
Sung Ryul Shim
Epidemiol Health. 2022;44:e2022083.   Published online September 28, 2022
DOI: https://doi.org/10.4178/epih.e2022083
  • 1,436 View
  • 136 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
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.
Summary
Korean summary
본 연구는 진단검사 메타분석시 개별 대상연구가 다수의 기준치 (cut-off value, threshold)에 따른 여러 개의 효과크기를 제공하고 있을 때, 정보의 누락없이 이를 모두 사용하는 방법과 더불어 기존의 수행방법도 비교 분석하였기에 연구에 적합한 모델 선택에 도움이 될 것이다.
Key Message
The objective of this study is to introduce methods to use all of the information without omission when indi¬vidual studies provide multiple effect sizes according to multiple cut-off values (thresholds) during diagnostic test accuracy (DTA) for data integration.
Dose-response meta-analysis: application and practice using the R software
Sung Ryul Shim, Jonghoo Lee
Epidemiol Health. 2019;41:e2019006.   Published online March 28, 2019
DOI: https://doi.org/10.4178/epih.e2019006
  • 12,821 View
  • 448 Download
  • 15 Citations
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
The objective of this study was to describe the general approaches of dose-response meta-analysis (DRMA) available for the quantitative synthesis of data using the R software. We conducted a DRMA using two types of data, the difference of means in continuous data and the odds ratio in binary data. The package commands of the R software were “doseresmeta” for the overall effect sizes that were separated into a linear model, quadratic model, and restricted cubic split model for better understanding. The effect sizes according to the dose and a test for linearity were demonstrated and interpreted by analyzing one-stage and two-stage DRMA. The authors examined several flexible models of exposure to pool study-specific trends and made a graphical presentation of the dose-response trend. This study focused on practical methods of DRMA rather than theoretical concepts for researchers who did not major in statistics. The authors hope that this study will help many researchers use the R software to perform DRMAs more easily, and that related research will be pursued.
Summary
Korean summary
통계프로그램으로서의 R이 대세인 연구환경에서 R을 활용한 체계적인 메타분석 안내서가 요구되어지며 본 연구는 이에 대한 좋은 해답이 될 것이다. 더욱이 본 연구는 용량-반응 메타분석 (Dose-response meta-analysis)의 이론과 더불어 R을 활용한 실습위주의 내용으로 구성되어져 있어 누구나 쉽게 실행할 수 있을 것이다. 본 연구를 통하여 많은 연구자들이 보다 쉽게 본인의 연구분야에 적용하여 관련 연구가 활성화되기를 바란다.
Key Message

Citations

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Diagnostic test accuracy: application and practice using R software
Sung Ryul Shim, Seong-Jang Kim, Jonghoo Lee
Epidemiol Health. 2019;41:e2019007.   Published online March 28, 2019
DOI: https://doi.org/10.4178/epih.e2019007
  • 14,994 View
  • 538 Download
  • 32 Citations
AbstractAbstract AbstractSummary PDFSupplementary Material
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.
Summary
Korean summary
통계프로그램으로서의 R이 대세인 연구환경에서 R을 활용한 체계적인 메타분석 안내서가 요구되어지며 본 연구는 이에 대한 좋은 해답이 될 것이다. 더욱이 본 연구는 진단검사 메타분석 (Diagnostic test accuracy)의 이론과 더불어 R을 활용한 실습위주의 내용으로 구성되어져 있어 누구나 쉽게 실행할 수 있을 것이다. 본 연구를 통하여 많은 연구자들이 보다 쉽게 본인의 연구분야에 적용하여 관련 연구가 활성화되기를 바란다.
Key Message

Citations

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Intervention meta-analysis: application and practice using R software
Sung Ryul Shim, Seong-Jang Kim
Epidemiol Health. 2019;41:e2019008.   Published online March 28, 2019
DOI: https://doi.org/10.4178/epih.e2019008
  • 17,017 View
  • 503 Download
  • 31 Citations
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.
Summary
Korean summary
통계프로그램으로서의 R이 대세인 연구환경에서 R을 활용한 체계적인 메타분석 안내서가 요구되어지며 본 연구는 이에 대한 좋은 해답이 될 것이다. 더욱이 본 연구는 메타분석 (Intervention meta-analysis)의 이론과 더불어 R을 활용한 실습위주의 내용으로 구성되어져 있어 누구나 쉽게 실행할 수 있을 것이다. 본 연구를 통하여 많은 연구자들이 보다 쉽게 본인의 연구분야에 적용하여 관련 연구가 활성화되기를 바란다.
Key Message

Citations

Citations to this article as recorded by  
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Network meta-analysis: application and practice using R software
Sung Ryul Shim, Seong-Jang Kim, Jonghoo Lee, Gerta Rücker
Epidemiol Health. 2019;41:e2019013.   Published online April 8, 2019
DOI: https://doi.org/10.4178/epih.e2019013
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AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Summary
Korean summary
통계프로그램으로서의 R이 대세인 연구환경에서 R을 활용한 체계적인 메타분석 안내서가 요구되어지며 본 연구는 이에 대한 좋은 해답이 될 것이다. 더욱이 본 연구는 네트워크 메타분석 (Network meta-analysis)의 이론과 더불어 R을 활용한 실습위주의 내용으로 구성되어져 있어 누구나 쉽게 실행할 수 있을 것이다. 본 연구를 통하여 많은 연구자들이 보다 쉽게 본인의 연구분야에 적용하여 관련 연구가 활성화되기를 바란다.
Key Message

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