The Galaxy Plot: A New Visualization Tool for Bivariate Meta-Analysis Studies

Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov

Multivariate meta-analysis (MMA) is a powerful statistical technique that can provide more reliable and informative results than traditional univariate meta-analysis, which allows for comparisons across outcomes with increased statistical power. However, implementing appropriate statistical methods for MMA can be challenging due to the requirement of various specific tasks in data preparation. The metavcov package aims for model preparation, data visualization, and missing data solutions to provide tools for different methods that cannot be found in accessible software. It provides sufficient constructs for estimating coefficients from other well-established packages. For model preparation, users can compute both effect sizes of various types and their variance-covariance matrices, including correlation coefficients, standardized mean difference, mean difference, log odds ratio, log risk ratio, and risk difference. The package provides a tool to plot the confidence intervals for the primary studies and the overall estimates. When specific effect sizes are missing, single imputation is available in the model preparation stage; a multiple imputation method is also available for pooling the results in a statistically principled manner from models of users' choice. The package is demonstrated in two real data applications and a simulation study to assess methods for handling missing data.

Diagnostic performances of common nucleic acid tests for SARS-CoV-2 in hospitals and clinics: a systematic review and meta-analysis

BACKGROUND

An optimised standard experimental setup across different hospitals is urgently needed to ensure consistency in nucleic acid test results for SARS-CoV-2 detection. A standard comparison across different nucleic acid tests and their optimal experimental setups is not present. We assessed the performance of three common nucleic acid tests, namely digital PCR (dPCR), quantitative PCR (qPCR), and loop-mediated isothermal amplification (LAMP), to detect SARS-CoV-2 in clinical settings.

METHODS

In this systematic review and meta-analysis we compared sensitivity and specificity of qPCR, dPCR, and LAMP and their performances when different experimental setups (namely specimen type used, use of RNA extraction, primer-probe sets, and RNA extraction methods) are applied. We searched PubMed, BioRxiv, MedRxiv, SciFinder, and ScienceDirect for studies and preprints published between Feb 29 and Dec 15, 2020. Included dPCR, qPCR, and LAMP studies using any type of human specimens should report the number of true-positive, true-negative, false-positive, and false-negative cases with Emergency Use Authorization (EUA)-approved PCR assays as the comparator. Studies with a sample size of less than ten, descriptive studies, case studies, reviews, and duplicated studies were excluded. Pooled sensitivity and specificity were computed from the true and false positive and negative cases using Reitsma's bivariate random-effects and bivariate latent class models. Test performance reported in area under the curve (AUC) of the three nucleic acid tests was further compared by pooling studies with similar experimental setups (eg, tests that used RNA extracted pharyngeal swabs but with either the open reading frame 1ab or the N primer). Heterogeneity was assessed and reported in I 2 and τ2.

FINDINGS

Our search identified 1277 studies of which we included 66 studies (11 dPCR, 32 qPCR, and 23 LAMP) with 15 017 clinical samples in total in our systematic review and 52 studies in our meta-analysis. dPCR had the highest pooled diagnostic sensitivity (94·1%, 95% CI 88·9-96·6, by Reitsma's model and 95·8%, 54·9-100·0, by latent class model), followed by qPCR (92·7%, 88·3-95·6, and 93·4%, 60·9-99·9) and LAMP (83·3%, 76·9-88·2, and 86·2%, 20·7-99·9), using EUA-approved PCR kits as the reference standard. LAMP was the most specific with a pooled estimate of 96·3% (93·8-97·8) by Reitsma's model and 94·3% (49·1-100·0) by latent class model, followed by qPCR (92·9%, 87·2-96·2, and 93·1%, 47·1-100·0) and dPCR (78·5%, 57·4-90·8, and 73·8%, 0·9-100·0). The overall heterogeneity was I 2 0·5% (τ2 2·79) for dPCR studies, 0% (4·60) for qPCR studies, and 0% (3·96) for LAMP studies. AUCs of the three nucleic acid tests were the highest and differed the least between tests (ie, AUC>0·98 for all tests) when performed with RNA extracted pharyngeal swabs using SARS-CoV-2 open reading frame 1ab primer.

INTERPRETATION

All three nucleic acid tests consistently perform better with pharyngeal swabs using SARS-CoV-2 open reading frame 1ab primer with RNA extraction. dPCR was shown to be the most sensitive, followed by qPCR and LAMP. However, their accuracy does not differ significantly. Instead, accuracy depends on specific experimental conditions, implying that more efforts should be directed to optimising the experimental setups for the nucleic acid tests. Hence, our results could be a reference for optimising and establishing a standard nucleic acid test protocol that is applicable in laboratories worldwide.

FUNDING

University Grants Committee and The Chinese University of Hong Kong.

Application of network meta-analysis in the field of physical activity and health promotion

Continued advancement in the field of physical activity and health promotion relies heavily on the synthesis of rigorous scientific evidence. As such, systematic reviews and meta-analyses of randomized controlled trials have led to a better understanding of which intervention strategies are superior (i.e., produce the greatest effects) in physical activity-based health behavior change interventions. Indeed, standard meta-analytic approaches have allowed researchers in the field to synthesize relevant experimental evidence using pairwise procedures that produce reliable estimates of the homogeneity, magnitude, and potential biases in the observed effects. However, pairwise meta-analytic procedures are only capable to discerning differences in effects between a select intervention strategy and a select comparison or control condition. In order to maximize the impact of physical activity interventions on health-related outcomes, it is necessary to establish evidence concerning the comparative efficacy of all relevant physical activity intervention strategies. The development of network meta-analysis (NMA)-most commonly used in medical-based clinical trials-has allowed for the quantification of indirect comparisons, even in the absence of direct, head-to-head trials. Thus, it stands to reason that NMA can be applied in physical activity and health promotion research to identify the best intervention strategies. Given that this analysis technique is novel and largely unexplored in the field of physical activity and health promotion, care must be taken in its application to ensure reliable estimates and discernment of the effect sizes among interventions. Therefore, the purpose of this review is to comment on the potential application and importance of NMA in the field of physical activity and health promotion, describe how to properly and effectively apply this technique, and suggest important considerations for its appropriate application in this field. In this paper, overviews of the foundations of NMA and commonly used approaches for conducting NMA are provided, followed by assumptions related to NMA, opportunities and challenges in NMA, and a step-by-step example of developing and conducting an NMA.

A historical review of publication bias

Publication bias is a well-known threat to the validity of meta-analyses and, more broadly, the reproducibility of scientific findings. When policies and recommendations are predicated on an incomplete evidence base, it undermines the goals of evidence-based decision-making. Great strides have been made in the last 50 years to understand and address this problem, including calls for mandatory trial registration and the development of statistical methods to detect and correct for publication bias. We offer an historical account of seminal contributions by the evidence synthesis community, with an emphasis on the parallel development of graph-based and selection model approaches. We also draw attention to current innovations and opportunities for future methodological work.

Testing small study effects in multivariate meta-analysis

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias, and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error rates with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed tests with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the Cochrane Database.