Graphical augmentations to sample‐size‐based funnel plot in meta‐analysis

Lin, Lifeng

doi:10.1002/jrsm.1340

Cited by 31 publications

(28 citation statements)

References 65 publications

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“…We did not assess for publication bias because standard methods such as funnel plots and associated tests were developed for comparative studies and therefore do not produce reliable results for meta-analysis of proportions. (19, 20)…”

Section: Methodsmentioning

confidence: 99%

Prevalence and outcomes of co-infection and super-infection with SARS-CoV-2 and other pathogens: A Systematic Review and Meta-analysis

Musuuza

Watson

Parmasad

et al. 2020

Preprint

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Introduction: The recovery of other respiratory viruses in patients with SARS-CoV-2 infection has been reported, either at the time of a SARS-CoV-2 infection diagnosis (co-infection) or subsequently (superinfection). However, data on the prevalence, microbiology and outcomes of co-infection and super infection are limited. The purpose of this study was to examine occurrence of respiratory co-infections and superinfections and their outcomes among patients with SARS-CoV-2 infection. Patients and Methods: We searched literature databases for studies published from October 1, 2019, through June 11, 2020. We included studies that reported clinical features and outcomes of co-infection or super-infection of SARS-CoV-2 and other pathogens in hospitalized and non-hospitalized patients. We followed PRISMA guidelines and we registered the protocol with PROSPERO as: CRD42020189763. Results: Of 1310 articles screened, 48 were included in the random effects meta-analysis. The pooled prevalence of co-infection was 12% (95% confidence interval (CI): 6%-18%, n=29, I2=98%) and that of super-infection was 14% (95% CI: 9%-21%, n=18, I2=97%). Pooled prevalence of pathogen type stratified by co- or super-infection: viral co-infections, 4% (95% CI: 2%-7%); viral super-infections, 2% (95% CI: 0%-7%); bacterial co-infections, 4% (95% CI: 1%-8%); bacterial super-infections, 6% (95% CI: 2%-11%); fungal co-infections, 4% (95% CI: 1%-8%); and fungal super-infections, 4% (95% CI: 0%-11%). Compared to those with co-infections, patients with super-infections had a higher prevalence of mechanical ventilation [21% (95% CI: 13%-31%) vs. 7% (95% CI: 2%-15%)] and greater average length of hospital stay [mean=12.5 days, standard deviation (SD) =5.3 vs. mean=10.2 days, SD= 6.7]. Conclusions: Our study showed that as many as 14% of patients with COVID-19 have super-infections and 12% have co-infections. Poor outcomes were associated with super-infections. Our findings have implications for diagnostic testing and therapeutics, particularly in the upcoming respiratory virus season in the Northern Hemisphere.

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Section: Methodsmentioning

confidence: 99%

Prevalence and outcomes of co-infection and super-infection with SARS-CoV-2 and other pathogens: A Systematic Review and Meta-analysis

Musuuza

Watson

Parmasad

et al. 2020

Preprint

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“…The funnel plot is widely and frequently used in meta-analyses for assessing publication bias [16] ; it is a scatter plot with studies’ effect sizes on the horizontal axis and their standard errors (or other measures of precision, e.g., sample sizes) on the vertical axis. [17–19] The funnel plot is supposed to be symmetrical if no publication bias appears. [9] Missing studies suppressed by publication bias in a meta-analysis usually lead to a noticeable asymmetrical funnel plot.…”

Section: Introductionmentioning

confidence: 99%

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

2019

Self Cite

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Publication bias is a type of systematic error when synthesizing evidence that cannot represent the underlying truth. Clinical studies with favorable results are more likely published and thus exaggerate the synthesized evidence in meta-analyses. The trim-and-fill method is a popular tool to detect and adjust for publication bias. Simulation studies have been performed to assess this method, but they may not fully represent realistic settings about publication bias. Based on real-world meta-analyses, this article provides practical guidelines and recommendations for using the trim-and-fill method. We used a worked illustrative example to demonstrate the idea of the trim-and-fill method, and we reviewed three estimators ( R 0 , L 0 , and Q 0 ) for imputing missing studies. A resampling method was proposed to calculate P values for all 3 estimators. We also summarized available meta-analysis software programs for implementing the trim-and-fill method. Moreover, we applied the method to 29,932 meta-analyses from the Cochrane Database of Systematic Reviews , and empirically evaluated its overall performance. We carefully explored potential issues occurred in our analysis. The estimators L 0 and Q 0 detected at least one missing study in more meta-analyses than R 0 , while Q 0 often imputed more missing studies than L 0 . After adding imputed missing studies, the significance of heterogeneity and overall effect sizes changed in many meta-analyses. All estimators generally converged fast. However, L 0 and Q 0 failed to converge in a few meta-analyses that contained studies with identical effect sizes. Also, P values produced by different estimators could yield different conclusions of publication bias significance. Outliers and the pre-specified direction of missing studies could have influential impact on the trim-and-fill results. Meta-analysts are recommended to perform the trim-and-fill method with great caution when using meta-analysis software programs. Some default settings (e.g., the choice of estimators and the direction of missing studies) in the programs may not be optimal for a certain meta-analysis; they should be determined on a case-by-case basis. Sensitivity analyses are encouraged to examine effects of different estimators and outlying studies. Also, the trim-and-fill estimator should be routinely reported in meta-analyses, because the results depend highly on it.

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“…Next, cautious and stepwise clinical trials may be considered to assess the efficacy of MNX1-AS1-targeting drugs in cancer patients. Seventh, many methodologies are available to evaluate publication bias [39,[41][42][43][44], but only Egger's linear regression test and Begg's rank test were applied in our meta-analysis. Thus, our publication bias evaluation may be not enough reliable, though both Egger's linear regression test and Begg's rank test indicated no significant publication bias in the current meta-analysis.…”

Section: Discussionmentioning

confidence: 99%

Clinical significance of long noncoding RNA MNX1-AS1 in human cancers: a meta-analysis of cohort studies and bioinformatics analysis based on TCGA datasets

et al. 2021

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MNX1-AS1 expression has been proposed to be abnormally upregulated in multiple human malignancies and be linked with the survival outcome of patients. However, relevant conclusions were yielded based on the limited samples. Therefore, we herein implemented a meta-analysis of the published cohort studies to further decipher the relationship of MNX1-AS1 level to prognosis and clinicopathological features in various cancers. Additionally, using The Cancer Genome Atlas (TCGA) datasets we carried out a bioinformatics analysis to make a further evaluation on the prognostic value of MNX1-AS1 expression. The results of meta-analysis indicated elevated MNX1-AS1 level closely correlated with poorer overall survival (OS) (HR = 1.97, 95% CI, 1.73-2.24; P < 0.00001), and disease-free survival (DFS) (HR = 2.24, 95% CI, 1.48-3.38; P = 0.0001) in cancers, which was confirmed by the bioinformatics analysis. Besides, it was observed the upregulated MNX1-AS1 level was significantly related to invasion depth, disease stage, tumor metastasis, and differentiation. Collectively, high MNX1-AS1 level correlated with poor survival outcome and aggressive clinicopathological characteristics in various cancers, suggesting that MNX1-AS1 may be applied as a prognostic marker and even a therapeutic target. Nevertheless, more high-quality studies designed with a large sample size should be conducted to further determine the clinical role of MNX1-AS1 in specific cancer types.

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Graphical augmentations to sample‐size‐based funnel plot in meta‐analysis

Cited by 31 publications

References 65 publications

Prevalence and outcomes of co-infection and super-infection with SARS-CoV-2 and other pathogens: A Systematic Review and Meta-analysis

Prevalence and outcomes of co-infection and super-infection with SARS-CoV-2 and other pathogens: 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

Clinical significance of long noncoding RNA MNX1-AS1 in human cancers: a meta-analysis of cohort studies and bioinformatics analysis based on TCGA datasets

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