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.
Hope ; Linear Models ; Odds Ratio

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.
Hope ; Linear Models ; Odds Ratio

Objective: The World Health Organization (WHO) declared a pandemic on March 11, 2020 after more than 118,000 cases of coronavirus disease 2019 (COVID-19) had been reported in 114 countries. Our study analyzed the cumulative incidence rate based on WHO data starting with the first confirmed patient until the peak of transmission. In addition, the numerical values of nasometry from normal subjects were quantified to analyze the linguistic features. Methods: This study consisted of two main methodologies including a meta-analysis based on nasometry data involving normal adults and cumulative incidence rate based on WHO data. In addition, the numerical values of nasometry from normal subjects were quantified to analyze the linguistic features. Results: The pooled overall mean differences (MDs) for oral text nasalance among linguistic families were 14.655 (95% confidence interval [CI], 7.986–21.324) in Arabic, 24.441 (95% CI, 17.920–30.962) in Chinese, 14.964 (95% CI, 13.677–16.251) in European, and 11.437 (95% CI, 9.880–12.994) in Ural-Altaic. The pooled overall MDs for cumulative incidence rate of COVID-19 were 190.3 (95% CI, 56.10–324.60) in Arabic, 283.20 (95% CI, 1.80–564.60) in European, and 5.70 (95% CI, 4.90–6.60) in Ural-Altaic. Correlation between oral nasalance score and cumulative incidence was significant (P=0.0004). Conclusion Our study showed the possible association between language characteristics and early spread of COVID-19. Further studies are needed to validate our outcomes based on various epidemiologic and behavioral factors including mask wearing.

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.

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.
Forests ; Hope ; Odds Ratio ; Population Characteristics ; Publication Bias

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.
Diagnostic Tests, Routine ; Forests ; Hope ; Odds Ratio ; Population Characteristics ; ROC Curve ; Sensitivity and Specificity

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.
Forests ; Hope ; Odds Ratio ; Population Characteristics ; Publication Bias

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.
Diagnostic Tests, Routine ; Forests ; Hope ; Odds Ratio ; Population Characteristics ; ROC Curve ; Sensitivity and Specificity

Purpose: Investigation of myopic open-angle glaucoma (OAG) prevalence in Northeast Asia by systematic review and meta-analysis. Methods: Systematic PubMed, Embase and Cochrane database searches for Northeast Asian population-based studies published up to 30 November 2020 and reporting on myopia and OAG diagnosis. By random-effect models, pooled OAG prevalence in a myopic population and pooled myopic OAG prevalence in a general population were generated, with 95% confidence intervals (CIs). Results: The meta-analysis encompassed five population-based studies in four countries (12,830 individuals, including 7,723 patients with myopia and 1,112 patients with OAG). In a myopic population, OAG prevalence was 4.10% (95% CI, 3.00–5.70; I2 = 93%); in a general population, myopic OAG prevalence was 1.10% (95% CI, 0.60–1.70; I2 = 94%). A visual examination of funnel plot symmetry raised a suspicion of publication bias. Notwithstanding, Begg and Mazumbar’s adjusted rank correlation test showed no such evidence (p = 0.6242). Conclusions Our systematic review and meta-analysis returned an estimate of OAG prevalence in a myopic Northeast Asian population. Our findings will inform future glaucoma studies as well as public health guidelines for Northeast Asian populations.

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.