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Coronavirus Disease 2019 (COVID-19) is a disease caused by a new type of virus called Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Based on data up to April 9th 2020, confirmed cases of COVID-19 in East Java are 6.78% of the total confirmed cases in Indonesia. Meanwhile, cases of death due to COVID-19 are 6.07% of the total cases of death in Indonesia. In handling it, the Ministry of Health of the Republic of Indonesia classifies COVID-19 patients into 3 status levels, namely ODP, PDP and Confirmed. The results of the meta-analysis using the risk ratio with the Mantel-Haenszel fixed effect model obtained a pooled risk ratio for the PDP and ODP groups (RR = 33.66, 95% CI: 20.88-54.27), the confirmed and ODP groups (RR = 56.31, 95% CI: 32.07-98.87), and the confirmed and PDP groups (RR = 1.75, 95% CI: 1.2-2.53). The order of the group with the highest risk of death is Confirmed > PDP > ODP.

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Quand plusieurs études par comparaison de groupes (intervention et témoin) portent sur une même intervention en milieu scolaire, la synthèse de leurs résultats permet d’évaluer l’effet de cette intervention et de répondre aux attentes des praticiens, mais aussi d’orienter les recherches futures. C’est dans ce but que sont réalisées des synthèses quantitatives ou encore des méta-analyses. Pour chacune des études sélectionnées, une taille d’effet est calculée, qui est le g de Hedges (différence standardisée des moyennes des deux groupes) quand la variable à expliquer est un score post-test. La taille d’effet globale est alors estimée en suivant dans la plupart des cas le modèle des effets aléatoires. Enfin, pour conclure quant à l’intérêt de l’intervention, les tailles d’effet sont traduites en nombre de mois de progrès. Un exemple numérique est proposé pour faciliter la compréhension des modèles analytiques mis en oeuvre dans une méta-analyse pour les cas les plus simples.

In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other's effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package metacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples. [ABSTRACT FROM AUTHOR]

A few years ago, we developed metaan, a package to perform fixedor random-effects meta-analysis. In terms of random-effects meta-analysis, it offered a wide choice of models, including maximum likelihood, profile likelihood, or restricted maximum-likelihood, in addition to the established DerSimonian–Laird method available in metan or Cochrane's RevMan software. Other unique features included a wide range of reported heterogeneity measures and a plot of the maximum likelihood function. Since then, metaan has been continuously updated to offer improved graphics, more options, and more meta-analysis models. In this necessary update, we describe these additions and discuss the new models and the evidence behind them. [ABSTRACT FROM AUTHOR]

Objective Data: Preconception or early administration of low-dose aspirin might improve endometrial growth, placental vascularization, and organogenesis. Most studies have evaluated the potential benefit of preconception or early administration of low-dose aspirin in women with a history of recurrent pregnancy loss, women who have undergone in vitro fertilization, or women with thrombophilia or antiphospholipid syndrome. These women are at an increased risk of placenta-associated complications of pregnancy, including preeclampsia, preterm delivery, and fetal growth restriction.Study Outcomes: We performed a systematic review and meta-analysis to evaluate the effect of low-dose aspirin initiated at <11 weeks' gestation on the risk of preeclampsia, gestational hypertension, or any hypertensive disorder of pregnancy. Secondary outcomes included preterm delivery at <37 weeks' gestation and fetal growth restriction.Study Appraisal and Synthesis Methods: We searched in MEDLINE via PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform from 1985 to November 2018. Entry criteria were randomized controlled trials evaluating the effect of aspirin administered at <11 weeks' gestation in preventing preeclampsia and/or hypertensive disorders in pregnancy or improving pregnancy outcomes in women with recurrent miscarriage as compared with placebo or no treatment and outcome data available or provided by authors for >85% of the study population. Relative risks with 95% confidence intervals were calculated for each study and pooled for global analysis as the effect measure. We assessed statistical heterogeneity in each meta-analysis using the χ2 statistics, I2, and Tau2. Heterogeneity was considered substantial if an I2 was greater than 50% and either the Tau2 was greater than zero or there was a low P value (<0.10) in the χ2 test for heterogeneity. Random-effects meta-analysis, weighted by the size of the studies, was performed to produce an overall summary on aspirin effect for each outcome. Sensitivity analysis by sequential omission of each individual study and by fixed-effects model was performed. Publication bias was not assessed because of the small number of included studies. Statistical analysis was performed using Stata release 14.0 (StataCorp).Results: The entry criteria were fulfilled by 8 randomized controlled trials on a combined total of 1426 participants. Low-dose aspirin initiated at <11 weeks' gestation was associated with a nonsignificant reduction in the risk of preeclampsia (relative risk, 0.52; 95% confidence interval, 0.23-1.17, P = .115), gestational hypertension (relative risk, 0.49; 95% confidence interval, 0.20-1.21; P = .121), and any hypertensive disorder of pregnancy (relative risk, 0.59; 95% confidence interval, 0.33-1.04, P = .067). Early administration of low-dose aspirin reduced the risk of preterm delivery (relative risk, 0.52; 95% confidence interval, 0.27-0.97, P = .040) but had no impact on the risk of fetal growth restriction (relative risk, 1.10; 95% confidence interval, 0.58-2.07, P = .775). Except for preterm delivery and any hypertensive disorder of pregnancy, sensitivity analysis demonstrated similar observations, therefore confirming the robustness of the analysis.Conclusion: The administration of low-dose aspirin at <11 weeks' gestation in women at high risk does not decrease the risk of preeclampsia, gestational hypertension, any hypertensive disorder of pregnancy, and fetal growth restriction. However, it might reduce the risk of preterm delivery. Larger randomized controlled trials will be required to substantiate the findings. [ABSTRACT FROM AUTHOR]

In meta‐analytic studies, there are often multiple moderators available (eg, study characteristics). In such cases, traditional meta‐analysis methods often lack sufficient power to investigate interaction effects between moderators, especially high‐order interactions. To overcome this problem, meta‐CART was proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The aim of this study is to improve meta‐CART upon two aspects: 1) to integrate the two steps of the approach into one and 2) to consistently take into account the fixed‐effect or random‐effects assumption in both the the interaction identification and testing process. For fixed effect meta‐CART, weights are applied, and subgroup analysis is adapted. For random effects meta‐CART, a new algorithm has been developed. The performance of the improved meta‐CART was investigated via an extensive simulation study on different types of moderator variables (ie, dichotomous, nominal, ordinal, and continuous variables). The simulation results revealed that the new method can achieve satisfactory performance (power greater than 0.80 and Type I error less than 0.05) if appropriate pruning rule is applied and the number of studies is large enough. The required minimum number of studies ranges from 40 to 120 depending on the complexity and strength of the interaction effects, the within‐study sample size, the type of moderators, and the residual heterogeneity. [ABSTRACT FROM AUTHOR]

Meta-analysis has been widely used to synthesize information from related studies to achieve reliable findings. However, in studies of rare events, the event counts are often low or even zero, and so standard meta-analysis methods such as fixed-effect models with continuity correction may cause substantial bias in estimation. Recently, Bhaumik et al. developed a simple average estimator for the overall treatment effect based on a random effects model. They proved that the simple average method with the continuity correction factor 0.5 (SA_0.5) is the least biased for large samples and showed via simulation that it has superior performance when compared with other commonly used estimators. However, the random effects models used in previous work are restrictive because they all assume that the variability in the treatment group is equal to or always greater than that in the control group. Under a general framework that explicitly allows treatment groups with unequal variability but assumes no direction, we prove that SA_0.5 is still the least biased for large samples. Meanwhile, to account for a trade-off between the bias and variance in estimation, we consider the mean squared error to assess estimation efficiency and show that SA_0.5 fails to minimize the mean squared error. Under a new random effects model that accommodates groups with unequal variability, we thoroughly compare the performance of various methods for both large and small samples via simulation and draw conclusions about when to use which method in terms of bias, mean squared error, type I error, and confidence interval coverage. A data example of rosiglitazone meta-analysis is used to provide further comparison. [ABSTRACT FROM AUTHOR]

Abstract: Meta‐analyses conventionally weight study estimates on the inverse of their error variance, in order to maximize precision. Unbiased variability in the estimates of these study‐level error variances increases with the inverse of study‐level replication. Here, we demonstrate how this variability accumulates asymmetrically across studies in precision‐weighted meta‐analysis, to cause undervaluation of the meta‐level effect size or its error variance (the meta‐effect and meta‐variance). Small samples, typical of the ecological literature, induce big sampling errors in variance estimation, which substantially bias precision‐weighted meta‐analysis. Simulations revealed that biases differed little between random‐ and fixed‐effects tests. Meta‐estimation of a one‐sample mean from 20 studies, with sample sizes of 3–20 observations, undervalued the meta‐variance by c. 20%. Meta‐analysis of two‐sample designs from 20 studies, with sample sizes of 3–10 observations, undervalued the meta‐variance by 15%–20% for the log response ratio (lnR); it undervalued the meta‐effect by c. 10% for the standardized mean difference (SMD). For all estimators, biases were eliminated or reduced by a simple adjustment to the weighting on study precision. The study‐specific component of error variance prone to sampling error and not parametrically attributable to study‐specific replication was replaced by its cross‐study mean, on the assumptions of random sampling from the same population variance for all studies, and sufficient studies for averaging. Weighting each study by the inverse of this mean‐adjusted error variance universally improved accuracy in estimation of both the meta‐effect and its significance, regardless of number of studies. For comparison, weighting only on sample size gave the same improvement in accuracy, but could not sensibly estimate significance. For the one‐sample mean and two‐sample lnR, adjusted weighting also improved estimation of between‐study variance by DerSimonian‐Laird and REML methods. For random‐effects meta‐analysis of SMD from little‐replicated studies, the most accurate meta‐estimates obtained from adjusted weights following conventionally weighted estimation of between‐study variance. We recommend adoption of weighting by inverse adjusted‐variance for meta‐analyses of well‐ and little‐replicated studies, because it improves accuracy and significance of meta‐estimates, and it can extend the scope of the meta‐analysis to include some studies without variance estimates. [ABSTRACT FROM AUTHOR]

In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations. [ABSTRACT FROM AUTHOR]

Meta-analysis is a common tool for synthesizing results of multiple studies. Among methods for performing meta-analysis, the approach known as 'fixed effects' or 'inverse variance weighting' is popular and widely used. A common interpretation of this method is that it assumes that the underlying effects in contributing studies are identical, and for this reason it is sometimes dismissed by practitioners. However, other interpretations of fixed effects analyses do not make this assumption, yet appear to be little known in the literature. We review these alternative interpretations, describing both their strengths and their limitations. We also describe how heterogeneity of the underlying effects can be addressed, with the same minimal assumptions, through either testing or meta-regression. Recommendations for the practice of meta-analysis are given; it is hoped that these will foster more direct connection of the questions that meta-analysts wish to answer with the statistical methods they choose. [ABSTRACT FROM AUTHOR]

Meta-analysis has been widely applied to rare adverse event data because it is very difficult to reliably detect the effect of a treatment on such events in an individual clinical study. However, it is known that standard meta-analysis methods are often biased, especially when the background incidence rate is very low. A recent work by Bhaumik et al. proposed new moment-based approaches under a natural random effects model, to improve estimation and testing of the treatment effect and the between-study heterogeneity parameter. It has been demonstrated that for rare binary events, their methods have superior performance to commonly used meta-analysis methods. However, their comparison does not include any Bayesian methods, although Bayesian approaches are a natural and attractive choice under the random-effects model. In this article, we study a Bayesian hierarchical approach to estimation and testing in meta-analysis of rare binary events using the random effects model in Bhaumik et al. We develop Bayesian estimators of the treatment effect and the heterogeneity parameter, as well as hypothesis testing methods based on Bayesian model selection procedures. We compare them with the existing methods through simulation. A data example is provided to illustrate the Bayesian approach as well. [ABSTRACT FROM PUBLISHER]

This article examines an improved alternative to the random effects (RE) model for meta-analysis of heterogeneous studies. It is shown that the known issues of underestimation of the statistical error and spuriously overconfident estimates with the RE model can be resolved by the use of an estimator under the fixed effect model assumption with a quasi-likelihood based variance structure — the IVhet model. Extensive simulations confirm that this estimator retains a correct coverage probability and a lower observed variance than the RE model estimator, regardless of heterogeneity. When the proposed IVhet method is applied to the controversial meta-analysis of intravenous magnesium for the prevention of mortality after myocardial infarction, the pooled OR is 1.01 (95% CI 0.71–1.46) which not only favors the larger studies but also indicates more uncertainty around the point estimate. In comparison, under the RE model the pooled OR is 0.71 (95% CI 0.57–0.89) which, given the simulation results, reflects underestimation of the statistical error. Given the compelling evidence generated, we recommend that the IVhet model replace both the FE and RE models. To facilitate this, it has been implemented into free meta-analysis software called MetaXL which can be downloaded from www.epigear.com . [ABSTRACT FROM AUTHOR]

This study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Systematic reviews and meta-analyses allow for a more transparent and objective appraisal of the evidence. They may decrease the number of false-negative results and prevent delays in the introduction of effective interventions into clinical practice. However, as for any other tool, their misuse can result in severely misleading results. In this article, we discuss the main steps that should be taken when conducting systematic reviews and meta-analyses, namely the preparation of a review protocol, identification of eligible trials, and data extraction, pooling of treatment effects across trials, investigation of potential reasons for differences in treatment effects across trials, and complete reporting of the review methods and findings. We also discuss common pitfalls that should be avoided, including the use of quality assessment tools to derive summary quality scores, pooling of data across trials as if they belonged to a single large trial, and inappropriate uses of meta-regression that could result in misleading estimates of treatment effects because of regression to the mean or the ecological fallacy. If conducted and reported properly, systematic reviews and meta-analyses will increase our understanding of the strengths and weaknesses of the available evidence, which may eventually facilitate clinical decision making. [ABSTRACT FROM PUBLISHER]

When pooling the results from different studies, conventional fixed effect and random effect meta-analysis do not aim at representing populations. Instead, they build on the common literature synthesis perspective and provide a summary effect estimate, which combines the results from the studies included. The individual studies are weighted according to sample sizes and variance, but not population sizes. We demonstrate that, from the population perspective, the results can yield highly misleading conclusions when the common heterogeneity problem in effect estimates is given. We discuss bias, heterogeneity and generalization in single and multiple studies; we show the relevance of the population perspective in psychiatric health care; and demonstrate how weights can alternatively be defined by the study samples’ target population sizes. The approach is exemplified with a simplistic meta-analysis of the effect of Tai Chi based on a Taiwanese and a US sample. We show that the results can be very sensitive against the choice of the target population. No matter which method is used to pool heterogeneous effect estimates, a global summary estimate should not be used to make a global and uniform health decision. Instead, the determinants of heterogeneity should be investigated and differential advice provided. [ABSTRACT FROM AUTHOR]

Background: Pooled estimates of air pollution health effects are important drivers of environmental risk communications and political willingness. In China, there is a lack of review studies to provide such estimates for health impact assessments. Methods: We systematically searched the MEDLINE database using keywords of 80 major Chinese cities in Mainland China, Hong Kong and Taiwan on 30 June 2012, yielding 350 abstracts with 48 non-duplicated reports either in English or Chinese after screening. We pooled the relative risks (RR) per 10 μg/m3 of particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3). Results: For short-term effects, the pooled RR (p < 0.05) ranges were: 1.0031 (PM10) to 1.0140 (NO2) for all-cause mortality, 1.0034 (cardiopulmonary, PM10) to 1.0235 (influenza and pneumonia, SO2) for 9 specific-causes mortality, 1.0021 (cardiovascular, PM10) to 1.0162 (asthma, O3) for 5 specific-causes hospital admissions. For birth outcomes, the RR (p < 0.05) ranged from 1.0051 (stillbirth, O3) to 1.1189 (preterm-birth, SO2) and for long-term effect on mortality from 1.0150 (respiratory, SO2) to 1.0297 (respiratory, NO2). Publication bias was absent (Egger test: p = 0.326 to 0.624). Annual PM10 and NO2 concentrations were inversely associated with RR of mortality (p = 0.017-0.028). Conclusions: Evidence on short-term effects of air pollution is consistent and sufficient for health impact assessment but that on long-term effects is still insufficient. [ABSTRACT FROM AUTHOR]

Meta-analysis uses numerical tools to pool data and to estimate a summary effect size for the comparison of two interventions from a set of randomised controlled trials identified in a systematic review. An effect size is a single number that expresses the difference in outcome from the interventions. The most commonly used effect sizes for dichotomous outcomes, for example, mortality, are the odds ratio and the relative risk. The results of a meta-analysis are usually presented in a complex figure, known as a forest plot, which shows both the individual studies and the summary statistics. Sensitivity analyses are performed to clarify the effect of the experimental design bias on the effect size. Clinical and statistical heterogeneity of the included studies are explored by the additional tools of fixed effect versus random effects models and subgroup analyses. [ABSTRACT FROM AUTHOR]

We performed a meta-analysis to determine the most sensitive subtests of the WHO Neurobehavioral Core Test Battery (NCTB) when administered to Chinese workers. Extensive Chinese Biological Medical Database and MEDLINE searches, review of cited references, and discussion with other investigators were undertaken. Data were extracted from 39 eligible studies; the summary effects (effect sizes) were calculated using a fixed-effect model. Various exposure agents showed different sensitivities to the seven subtests of NCTB. For mercury-exposure, the Benton Visual Retention was the most sensitive subtest, with an effect size (95% CI) of 6.0 (4.4–7.6). For lead-exposure, the most sensitive subtests were the Pursuit Aiming II and Profile of Mood States, with effect sizes (95% CI) of 11.3 (8.3–14.3) and 10.6 (7.5–13.7), respectively. For organic solvents-exposure, Digit Span, Pursuit Aiming II, and Digit Symbol were the most sensitive subtests, with effect sizes (95− CI) of 4.7 (3.3–6.1), 4.6 (3.1–6.1), and 4.1 (2.7–5.5), respectively. [ABSTRACT FROM AUTHOR]