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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
  • 21,461 View
  • 612 Download
  • 50 Web of Science
  • 47 Crossref
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.

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Comparison of methods of extracting information for meta-analysis of observational studies in nutritional epidemiology
Jong-Myon Bae
Epidemiol Health. 2016;38:e2016003.   Published online January 11, 2016
DOI: https://doi.org/10.4178/epih.e2016003
  • 18,001 View
  • 252 Download
  • 10 Web of Science
  • 8 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
A common method for conducting a quantitative systematic review (QSR) for observational studies related to nutritional epidemiology is the “highest versus lowest intake” method (HLM), in which only the information concerning the effect size (ES) of the highest category of a food item is collected on the basis of its lowest category. However, in the interval collapsing method (ICM), a method suggested to enable a maximum utilization of all available information, the ES information is collected by collapsing all categories into a single category. This study aimed to compare the ES and summary effect size (SES) between the HLM and ICM.
METHODS
A QSR for evaluating the citrus fruit intake and risk of pancreatic cancer and calculating the SES by using the HLM was selected. The ES and SES were estimated by performing a meta-analysis using the fixedeffect model. The directionality and statistical significance of the ES and SES were used as criteria for determining the concordance between the HLM and ICM outcomes.
RESULTS
No significant differences were observed in the directionality of SES extracted by using the HLM or ICM. The application of the ICM, which uses a broader information base, yielded more-consistent ES and SES, and narrower confidence intervals than the HLM.
CONCLUSIONS
The ICM is advantageous over the HLM owing to its higher statistical accuracy in extracting information for QSR on nutritional epidemiology. The application of the ICM should hence be recommended for future studies.
Summary
Korean summary
영양역학과 관련하여 관찰연구 결과를 체계적 고찰을 할 때, 통상적으로 섭취가 가장 낮은 군을 기준으로 하여 가장 높은 군의 effect size (ES) 정보를 추출하는 Highest versus Lowest Method (HLM)을 적용한다. 그러나 주어진 정보를 최대한 활용한다는 점에서 구간의 정보들을 통합한 ES 정보로 메타분석에 적용하는 Interval Collapse Method (ICM)법이 제안되었다. HLM에 비하여 더 많은 정보를 활용한 ICM 적용을 한 결과, ES 뿐만 아니라 SES에서도 일치성을 보이면서 표준오차가 작아지는 현상을 확인하였다. 영양역학의 메타분석을 위해 추출하는 방법 중 정보를 보다 더 많이 활용하는 ICM이 HLM에 비하여 통계적 정확성의 이득을 더 갖기 때문에, 향후 적극 활성화할 필요가 있다.

Citations

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Original Article
A Review Study on Comparing Treatment Effects among Subgroups.
Seonwoo Kim, Minji Kim, Soon Young Lee
Korean J Epidemiol. 1999;21(1):104-110.
  • 5,037 View
  • 18 Download
AbstractAbstract PDF
Abstract
It is interested in examining treatment effect on a particular category of subjects or in comparing treatment effects among different subgroups as well as overall treatment effect due to heterogeneity of study subjects. Subgroup analyses are exceedingly common, but they are also often misleading. Conclusions based on subgroup analyses can do harm both when a particular category of people is denied effective treatment (a "false-negative" conclusion), and when ineffective or even harmful treatment is given to a subgroup of people (a "false-positive" conclusion). Because of the frequency and the importance of clinical application of subgroup analysis, researchers need to be cautious about doing subgroup analyses. This study presents guidelines to help conducting subgroup analyses correctly.
Summary

Epidemiol Health : Epidemiology and Health