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

Background/Aims: Although pneumococcal urinary antigen tests (PUATs) have universally been used for the diagnosis of pneumococcal pneumonia, data on the efficacy of these exams are limited. The objective of our study was to investigate the clinical impact of the PUAT in patients with community-onset pneumonia (CO-pneumonia). Methods: We conducted a retrospective cohort study of patients diagnosed with CO-pneumonia. Patients were classified according to their PUAT results and were matched using the propensity score-matching method. The primary outcome was 30-day mortality. Results: A total of 1,257 patients were identified and 163 (13.0%) demonstrated positive PUAT results. The sensitivity and specificity values of PUAT for overall pneumococcal pneumonia were 56.5% and 91.4%, respectively. In the full cohort, there were no significant differences in 30-day mortality between the two groups (6.1% in the positive PUAT group vs. 8.2% in the negative PUAT group, p = 0.357). However, in the propensity-matched cohort, the 30-day mortality rates were lower in the positive PUAT group (5.6% vs. 17.4%, p = 0.001). With respect to secondary outcomes, the proportion of patients with potentially drug-resistant pathogens, changes in antibiotics, and failure rates of initial antibiotic therapy were significantly lower in the positive PUAT group than in the negative PUAT group of the propensity-matched cohort. Conclusions We found that the sensitivity of the index test was low and specificity was high in this clinical setting. And our findings suggest that positive PUAT results may be associated with favorable clinical outcomes in patients with CO-pneumonia.

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.

No abstract available.
Catheters

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 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 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.

PURPOSE: Peramivir is the first intravenously administered neuramidase inhibitor for immediate delivery of an effective single-dose treatment in patients with influenza. However, limited data are available on intravenous (IV) peramivir treatment compared to oral oseltamivir for these patients. MATERIALS AND METHODS: With a systematic review and meta-analysis, we compared the efficacy of IV peramivir with oral oseltamivir for treatment of patients with seasonal influenza. MEDLINE, EMBASE, and Cochrane Central Register were searched for relevant clinical trials. RESULTS: A total of seven trials [two randomized controlled trials (RCTs) and five non-randomized observational trials] involving 1676 patients were finally analyzed. The total number of peramivir- and oseltamivir-treated patients was 956 and 720, respectively. Overall, the time to alleviation of fever was lower in the peramivir-treated group compared with the oseltamivir-treated group [mean difference (MD), −7.17 hours; 95% confidence interval (CI) −11.00 to −3.34]. Especially, pooled analysis of observational studies (n=4) and studies of outpatients (n=4) demonstrated the superiority of the peramivir-treated group (MD, -7.83 hours; 95% CI −11.81 to −3.84 and MD, −7.71 hours; 95% CI −11.61 to −3.80, respectively). Mortality, length of hospital stay, change in virus titer 48 hours after admission, and the incidence of adverse events in these patients were not significantly different between the two groups. CONCLUSION: IV peramivir therapy might reduce the time to alleviation of fever in comparison with oral oseltamivir therapy in patients with influenza; however, we could not draw clear conclusions from a meta-analysis because of the few RCTs available and methodological limitations.
Fever ; Humans ; Incidence ; Influenza, Human* ; Length of Stay ; Mortality ; Oseltamivir* ; Outpatients ; Seasons ; Viral Load

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.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias