Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.

Meta-analysis of prevalence

STUDY OBJECTIVES: No systematic review or meta-analysis has yet been conducted to examine the impact of the pandemic on the prevalence of sleep problems among the general population, healthcare workers, or COVID-19 patients. Therefore, this systematic review was conducted to assess the impact and prevalence of sleep problems among those categories. METHODS: APA PsycINFO; Cochrane; Cumulative Index to Nursing and Allied Health Literature (CINAHL); EBSCOhost; EMBASE; Google Scholar; MEDLINE; ProQuest Medical; ScienceDirect; Scopus; and Web of Science from 01 November 2019 to 05 July 2020. Additionally, four preprints servers (medRxiv.org; Preprints.org; psyarxiv.com; arXiv.org; biorxiv.org) were also searched for papers accepted after peer-review but not yet published and indexed. There was no language restriction. The random-effect models meta-analysis model were used with the DerSimonian and Laird methodology. RESULTS: Forty-four papers, involving a total of 54,231 participants from 13 countries, were judged relevant and contributed to the systematic review and meta-analysis of sleep problems during COVID-19. The global pooled prevalence rate of sleep problems among all populations was 35.7% [95%CI 29.4-42.4%]. COVID-19 patients appeared to be the most affected group, with a pooled rate of 74.8% [95%CI 28.7-95.6%]. Healthcare workers and the general population had comparative rates of sleep problems with rates of 36.0% [95%CI 21.1-54.2%] and 32.3% [95%CI 25.3-40.2%], respectively. CONCLUSIONS: The prevalence of sleep problems during the COVID-19 pandemic is high and approximately affect 40% of people from the general and healthcare populations. COVID-19 active patients appeared to have higher prevalence rates of sleep problems.

Sleep problems during the COVID-19 pandemic by population: a systematic review and meta-analysis.

Antibiotic prescribing in patients with COVID-19: rapid review and meta-analysis.

Background Obesity is common in patients with coronavirus disease 2019 (COVID-19). The effects of obesity on clinical outcomes of COVID-19 warrant systematical investigation. Objective This study explores the effects of obesity with the risk of severe disease among patients with COVID-19. Methods Body mass index (BMI) and degree of visceral adipose tissue (VAT) accumulation were used as indicators for obesity status. Publication databases including preprints were searched up to August 10, 2020. Clinical outcomes of severe COVID-19 included hospitalization, a requirement for treatment in an intensive care unit (ICU), invasive mechanical ventilation (IMV), and mortality. Risks for severe COVID-19 outcomes are presented as odds ratios (OR) and 95% confidence interval (95%CI) for cohort studies with BMI-defined obesity, and standardized mean difference (SMD) and 95%CI for controlled studies with VAT-defined excessive adiposity. Results A total of 45, 650 participants from 30 studies with BMI-defined obesity and 3 controlled studies with VAT-defined adiposity were included for assessing the risk of severe COVID-19. Univariate analyses showed significantly higher ORs of severe COVID-19 with higher BMI: 1.76 (95%: 1.21, 2.56, P = 0.003) for hospitalization, 1.67 (95%CI: 1.26, 2.21, P Conclusions Obesity increases risk for hospitalization, ICU admission, IMV requirement and death among patients with COVID-19. Further, excessive visceral adiposity appears to be associated with severe COVID-19 outcomes. These findings emphasize the need for effective actions by individuals, the public and governments to increase awareness of the risks resulting from obesity and how these are heightened in the current global pandemic.

Obesity in patients with COVID-19: a systematic review and meta-analysis.

The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses

Publication bias is a serious problem in systematic reviews and meta-analyses, which can affect the validity and generalization of conclusions. Currently, approaches to dealing with publication bias can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functions to adjust the overall effect size estimate and are usually employed as sensitivity analyses to assess the potential impact of publication bias. Funnel-plot-based methods include visual examination of a funnel plot, regression and rank tests, and the nonparametric trim and fill method. Although these approaches have been widely used in applications, measures for quantifying publication bias are seldom studied in the literature. Such measures can be used as a characteristic of a meta-analysis; also, they permit comparisons of publication biases between different meta-analyses. Egger's regression intercept may be considered as a candidate measure, but it lacks an intuitive interpretation. This article introduces a new measure, the skewness of the standardized deviates, to quantify publication bias. This measure describes the asymmetry of the collected studies' distribution. In addition, a new test for publication bias is derived based on the skewness. Large sample properties of the new measure are studied, and its performance is illustrated using simulations and three case studies.

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Quantifying publication bias in meta-analysis

Network meta-analysis is a powerful approach for synthesizing direct and indirect evidence about multiple treatment comparisons from a collection of independent studies. At present, the most widely used method in network meta-analysis is contrast-based, in which a baseline treatment needs to be specified in each study, and the analysis focuses on modeling relative treatment effects (typically log odds ratios). However, population-averaged treatment-specific parameters, such as absolute risks, cannot be estimated by this method without an external data source or a separate model for a reference treatment. Recently, an arm-based network meta-analysis method has been proposed, and the R package pcnetmeta provides user-friendly functions for its implementation. This package estimates both absolute and relative effects, and can handle binary, continuous, and count outcomes.

Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package.

Background Meta-analyses frequently include studies with small sample sizes. Researchers usually fail to account for sampling error in the reported within-study variances; they model the observed study-specific effect sizes with the within-study variances and treat these sample variances as if they were the true variances. However, this sampling error may be influential when sample sizes are small. This article illustrates that the sampling error may lead to substantial bias in meta-analysis results. Methods We conducted extensive simulation studies to assess the bias caused by sampling error. Meta-analyses with continuous and binary outcomes were simulated with various ranges of sample size and extents of heterogeneity. We evaluated the bias and the confidence interval coverage for five commonly-used effect sizes (i.e., the mean difference, standardized mean difference, odds ratio, risk ratio, and risk difference). Results Sampling error did not cause noticeable bias when the effect size was the mean difference, but the standardized mean difference, odds ratio, risk ratio, and risk difference suffered from this bias to different extents. The bias in the estimated overall odds ratio and risk ratio was noticeable even when each individual study had more than 50 samples under some settings. Also, Hedges’ g, which is a bias-corrected estimate of the standardized mean difference within studies, might lead to larger bias than Cohen’s d in meta-analysis results. Conclusions Cautions are needed to perform meta-analyses with small sample sizes. The reported within-study variances may not be simply treated as the true variances, and their sampling error should be fully considered in such meta-analyses.

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Bias Caused By Sampling Error In Meta-analysis With Small Sample Sizes

Decision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data. This study compares seven commonly used publication bias tests (i.e., Begg’s rank test, trim-and-fill, Egger’s, Tang’s, Macaskill’s, Deeks’, and Peters’ regression tests) based on 28,655 meta-analyses available in the Cochrane Library. Egger’s regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg’s rank test and the trim-and-fill method. The agreement among Tang’s, Macaskill’s, Deeks’, and Peters’ regression tests for binary outcomes was moderately strong (most κ’s were around 0.6). Tang’s and Deeks’ tests had fairly similar performance (κ > 0.9). The agreement among Begg’s rank test, the trim-and-fill method, and Egger’s regression test was weak or moderate (κ < 0.5). Given the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.

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Lifeng Lin

Papers

Quantifying publication bias in meta-analysis

The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses

Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package.

Bias Caused By Sampling Error In Meta-analysis With Small Sample Sizes

Empirical Comparison of Publication Bias Tests in Meta-Analysis