scholarly journals metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Nhan Thi Ho ◽  
Fan Li ◽  
Shuang Wang ◽  
Louise Kuhn
Keyword(s):  
Random Effects ◽  
Relative Abundance ◽  
Meta Analysis ◽  
R Package ◽  
Random Effects Models ◽  
Abundance Data

scholarly journals metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effect models

2018 ◽  
Author(s):  
Nhan Thi Ho ◽  
Fan Li
Keyword(s):  
Random Effects ◽  
High Throughput ◽  
Relative Abundance ◽  
High Throughput Sequencing ◽  
Meta Analysis ◽  
R Package ◽  
Standard Errors ◽  
Relative Abundances ◽  
Meta Analyses ◽  
Analysis Models

ABSTRACTBackgroundThe rapid growth of high-throughput sequencing-based microbiome profiling has yielded tremendous insights into human health and physiology. Data generated from high-throughput sequencing of 16S rRNA gene amplicons are often preprocessed into composition or relative abundance. However, reproducibility has been lacking due to the myriad of different experimental and computational approaches taken in these studies. Microbiome studies may report varying results on the same topic, therefore, meta-analyses examining different microbiome studies to provide robust results are important. So far, there is still a lack of implemented methods to properly examine differential relative abundances of microbial taxonomies and to perform meta-analysis examining the heterogeneity and overall effects across microbiome studies.ResultsWe developed an R package ‘metamicrobiomeR’ that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a zero-inflated beta (BEZI) family (GAMLSS-BEZI) for analysis of microbiome relative abundance datasets. Both simulation studies and application to real microbiome data demonstrate that GAMLSS-BEZI well performs in testing differential relative abundances of microbial taxonomies. Importantly, the estimates from GAMLSS-BEZI are log(odds ratio) of relative abundances between groups and thus are comparable between microbiome studies. As such, we also apply random effects meta-analysis models to pool estimates and their standard errors across microbiome studies. We demonstrate the meta-analysis workflow and highlight the utility of our package on four studies comparing gut microbiomes between male and female infants in the first six months of life.ConclusionsGAMLSS-BEZI allows proper examination of microbiome relative abundance data. Random effects meta-analysis models can be directly applied to pool comparable estimates and their standard errors to evaluate the heterogeneity and overall effects across microbiome studies. The examples and workflow using our metamicrobiomeR package are reproducible and applicable for the analyses and meta-analyses of other microbiome studies.

scholarly journals Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis

QJM ◽  
2021 ◽  
Author(s):  
Marco Zuin ◽  
Gianluca Rigatelli ◽  
Claudio Bilato ◽  
Carlo Cervellati ◽  
Giovanni Zuliani ◽  
...  
Keyword(s):  
Random Effects ◽  
Mortality Risk ◽  
Meta Analysis ◽  
Random Effect ◽  
Final Analysis ◽  
Short Term ◽  
Random Effects Models ◽  
Pooled Prevalence ◽  
Statistical Heterogeneity ◽  
Trim And Fill

Abstract Objective The prevalence and prognostic implications of pre-existing dyslipidaemia in patients infected by the SARS-CoV-2 remain unclear. To perform a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with pre-existing dyslipidaemia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on dyslipidaemia among COVID-19 survivors and non-survivors. The pooled prevalence of dyslipidaemia was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I2 statistic. Results Eighteen studies, enrolling 74.132 COVID-19 patients [mean age 70.6 years], met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 17.5% of cases (95% CI: 12.3-24.3%, p < 0.0001), with high heterogeneity (I2=98.7%). Pre-existing dyslipidaemia was significantly associated with higher risk of short-term death (OR: 1.69, 95% CI: 1.19-2.41, p = 0.003), with high heterogeneity (I2=88.7%). Due to publication bias, according to the Trim-and-Fill method, the corrected random-effect ORs resulted 1.61, 95% CI 1.13-2.28, p < 0.0001 (one studies trimmed). Conclusions Dyslipidaemia represents a major comorbidity in about 18% of COVID-19 patients but it is associated with a 60% increase of short-term mortality risk.

scholarly journals Association of bisphenol A with puberty timing: a meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hui Meng ◽  
Yunping Zhou ◽  
Yunxia Jiang
Keyword(s):  
Bisphenol A ◽  
Confidence Intervals ◽  
Random Effects ◽  
Strong Correlation ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Study Heterogeneity ◽  
The Relationship ◽  
Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

EXPRESS: A meta-analysis of aspirin and subarachnoid haemorrhage in patients with intracranial aneurysms yields different results to the general population

2021 ◽  
pp. 174749302110048
Author(s):  
Frederick Ewbank ◽  
Jacqueline Birks ◽  
Diederik Bulters
Keyword(s):  
General Population ◽  
Subarachnoid Haemorrhage ◽  
Confidence Intervals ◽  
Random Effects ◽  
Intracranial Aneurysms ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Random Effects Models ◽  
Hazard Ratios ◽  
Unruptured Intracranial Aneurysms

Abstract Background Some studies have shown a protective association between aspirin use and subarachnoid haemorrhage (SAH). Other studies have found no relationship or the reverse. These studies differ in their study populations and definitions of SAH. Aims Our aim was to establish 1) if there is an association between aspirin and SAH, 2) how this differs between the general population and those with intracranial aneurysms. Summary of review Studies reporting aspirin use and the occurrence of SAH were included and grouped based on population (general population vs aneurysm population). Odds ratios, hazard ratios and confidence intervals were combined in random-effects models. 11 studies were included. Overall, there was an association between aspirin and SAH (OR 0.68 [0.48, 0.96]). However, populations were diverse and heterogeneity between studies high (p<0.00001), questioning the validity of combining these studies and justifying analysis by population. In the general population there was no difference in aspirin use between individuals with and without SAH (OR 1.15 [0.96, 1.38]). In patients with intracranial aneurysms, aspirin use was greater in patients without SAH (OR 0.37 [0.24, 0.58]), although these studies were at higher risk of bias. Conclusions There is an association between aspirin use and SAH in patients with intracranial aneurysms. This apparent protective relationship is not seen in the general population. Prospective randomised studies are required to further investigate the effect of aspirin on unruptured intracranial aneurysms.

Abstract WP65: Low-Versus Standard-Dose Alteplase for AcuteIschemic Strokes Within 4.5 Hours: An updated Meta Analysis

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Huaguang Zheng ◽  
Xu Tong ◽  
Liping Liu ◽  
Zixiao Li ◽  
Xiaoling Liao ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Random Effects ◽  
Standard Dose ◽  
Controlled Trial ◽  
Meta Analysis ◽  
Tissue Type ◽  
Random Effects Models ◽  
Dose Treatment ◽  
Randomized Controlled ◽  
Type Plasminogen Activator

Background and Purpose: We performed a meta-analysis to compare the outcomes between lower dose and standard dose intravenous tissue-type plasminogen activator (tPA) for acute ischemic stroke in randomized and non- randomized controlled trials. Methods: We searched PubMed for relevant studies and calculated pooled odds ratios (ORs) using random effects models.The primary endpoint was good functional outcome[modified Rankin Scale (mRS) of 0-1] at 3 month after stroke onset. Other major end points were all-cause mortality and symptomatic intracerebral haemorrhage (sICH). Results: From 2010 to 2016, 7 Cohort studies and 1 randomized controlled trial (ENCHANTED trial) were pooled in meta-analysis. The lower tPA strategy was likely to be less effective than the standard dose treatment (OR=0.87; 95% confidence interval [CI], 0.73-1.04, P=0.136; I 2 =47.9%, P=0.044 in random effects models and OR=0.88; 95% CI 0.88-0.98,P=0.016 ;I 2 =0.0%, P=0.693 in non- random effects models after 2 cohort studies were excluded due to heterogeneity). No difference was found for mortality at 90 days (OR=0.87; 95% CI 0.74-1.03, P=0.102 ;I 2 =0.0%, P=0.635 in non-random effects models)and sICH (OR=1.12; 95% CI 0.68-1.83,P=0.659; I 2 =57.6%, P=0.016 in random effects models and OR=1.23; 95% CI 0.92-1.65, P=0.168; I 2 =0.0%, P=0.547 in non-random effects models after 2 cohort studies were excluded due to heterogeneity ) between lower tPA group and standard dose . Conclusions: The low-dose alteplase strategy was less effective comparable to the standard-dose treatment .The safety was similar between the two strategies.

Statistical methods for detecting outlying and influential studies in meta-analysis of diagnostic test accuracy studies

2019 ◽  
Vol 29 (4) ◽  
pp. 1227-1242 ◽  
Author(s):  
Zelalem F Negeri ◽  
Joseph Beyene
Keyword(s):  
Diagnostic Test ◽  
Random Effects ◽  
Ad Hoc ◽  
Meta Analysis ◽  
Analysis Data ◽  
Diagnostic Test Accuracy ◽  
Statistical Reasoning ◽  
Test Accuracy ◽  
Analysis Tool ◽  
Random Effects Models

Bivariate random-effects models are currently widely used to synthesize pairs of test sensitivity and specificity across studies. Inferences drawn based on these models may be distorted in the presence of outlying or influential studies. Currently, subjective methods such as inspection of forest plots are used to identify outlying studies in meta-analysis of diagnostic test accuracy studies. We proposed objective methods based on solid statistical reasoning for identifying outlying and/or influential studies. The proposed methods have been validated using simulation study and illustrated on two published meta-analysis data. Our methods outperform and neglect the subjectivity of the currently used ad hoc methods. The proposed methods can be used as a sensitivity analysis tool concurrently with the current bivariate random-effects models or as a preliminary analysis tool for robust models that accommodate outlying and/or influential studies in meta-analysis of diagnostic test accuracy studies.

scholarly journals Multiple moderator meta-analysis using the R-package Meta-CART

2020 ◽  
Vol 52 (6) ◽  
pp. 2657-2673
Author(s):  
Xinru Li ◽  
Elise Dusseldorp ◽  
Xiaogang Su ◽  
Jacqueline J. Meulman
Keyword(s):  
Random Effects ◽  
Treatment Effectiveness ◽  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Tree Model ◽  
Effective Interventions ◽  
Look Ahead ◽  
Fixed And Random Effects ◽  
User Friendly

AbstractIn 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.

A basic introduction to fixed-effect and random-effects models for meta-analysis

2010 ◽  
Vol 1 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Michael Borenstein ◽  
Larry V. Hedges ◽  
Julian P.T. Higgins ◽  
Hannah R. Rothstein
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Fixed Effect ◽  
Random Effects Models

scholarly journals Prognostic Role of Matrix Metalloproteinases in Cervical Cancer: A Meta-Analysis

2021 ◽  
Vol 28 ◽  
pp. 107327482110337
Author(s):  
Weiwei Chen ◽  
Shenjiao Huang ◽  
Kun Shi ◽  
Lisha Yi ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Matrix Metalloproteinases ◽  
Random Effects ◽  
Fixed Effects ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Free Survival ◽  
Random Effects Models ◽  
Registration Number ◽  
Meta Analyses

Objective Studies have published the association between the expression of matrix metalloproteinases (MMPs) and the outcome of cervical cancer. However, the prognostic value in cervical cancer remains controversial. This meta-analysis was conducted to evaluate the prognostic functions of MMP expression in cervical cancer. Methods A comprehensive search of PubMed, Embase, and Web of Science databases was conducted to identify the eligible studies according to defined selection and excluding criteria and analyzed according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Fixed and random effects models were evaluated through the hazard ratios (HRs) and 95% confidence intervals (CIs) to estimate the overall survival (OS), recurrence-free survival (RFS), and progress-free survival (PFS). Results A total of 18 eligible studies including 1967 patients were analyzed for prognostic value. Totally 16 selected studies including 21 tests were relevant to the cervical cancer OS, 4 studies focused on RFS, and 1 study on PFS. The combined pooled HRs and 95% CIs of OS were calculated with random-effects models (HR = 1.64, 95% CI = 1.01–2.65, P = .000). In the subgroup analysis for OS, there was no heterogeneity in MMP-2 (I2 = .0%, P = .880), MMP-1 (I2 = .0%, P = .587), and MMP-14 (I2 = 28.3%, P = .248). In MMP-7 and MMP-9, the heterogeneities were obvious (I2 = 99.2% ( P = .000) and I2 = 77.9% ( P = .000), respectively). The pooled HRs and 95% CIs of RFS were calculated with fixed-effects models (HR = 2.22, 95% CI = 1.38–3.58, P = .001) and PFS (HR = 2.29, 95% CI = 1.14–4.58, P = .035). Conclusions The results indicated that MMP overexpression was associated with shorter OS and RFS in cervical cancer patients. It suggested that MMP overexpression might be a poor prognostic marker in cervical cancer. Research Registry Registration Number: reviewregistry 1159.

scholarly journals Association of GSTM1 and GSTT1 Null Deletions and GSTP1 rs1695 Polymorphism with the Risk of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis

2021 ◽  
Vol 20 (11) ◽  
Author(s):  
Mohammad Hossein Khosravi ◽  
Heidar Sharafi ◽  
Seyed Moayed Alavian
Keyword(s):  
Hepatocellular Carcinoma ◽  
Random Effects ◽  
Primary Liver Cancer ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Random Effects Models ◽  
Asian Populations ◽  
Liver Cancers ◽  
Glutathione S Transferases ◽  
Common Malignancy

Context: Hepatocellular carcinoma (HCC), as the most common type of primary liver cancer (accounting for 70% - 90% of all liver cancers), is the seventh most common malignancy worldwide. Glutathione S-transferases (GSTs) are a specific group of enzymes that are responsible for the detoxification of carcinogens. According to the available literature, genetic variations in this group of enzymes may be associated with the risk of HCC. In this study, we aimed to assess the association of GSTM1 and GSTT1 null deletions and GSTP1 rs1695 polymorphism with the risk of HCC. Methods: We systematically searched electronic databases, including PubMed, Scopus, and Web of Science, using appropriate keywords to gather relevant data until March 2019. Studies that met the inclusion criteria were included in the meta-analysis, using either fixed- or random-effects models based on the presence of heterogeneity. Results: This meta-analysis pooled 19 studies for GSTM1 null deletions, 14 studies for GSTT1 null deletions, and five studies for GSTP1 rs1695 polymorphism. In terms of heterogeneity, the pooled odds ratio (OR) was calculated in a random-effects model for both Asian and non-Asian populations. HCC was found to be associated with GSTM1 null deletions (OR = 1.26, 95% CI: 1.00 - 1.58, P = 0.05) and GSTT1 null deletions (OR = 1.39, 95% CI: 1.10 - 1.74, P = 0.005); however, no significant association was found between HCC and GSTP1 rs1695 polymorphism (OR = 1.14, 95% CI: 0.86 - 1.50, P = 0.36). Conclusions: We found that GSTM1 and GSTT1 null deletions increased the risk of HCC; however, the GSTP1 rs1695 polymorphism did not have a similar effect.

Sign in / Sign up

Export Citation Format

Share Document