DOCSLIB.ORG

Graphical Methods for Detecting Bias in Meta-Analysis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Graphical Methods for Detecting Bias in Meta-Analysis See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/13513488 Graphical methods for detecting bias in meta-analysis Article in Family medicine · October 1998 Source: PubMed CITATIONS READS 26 702 1 author: Robert Ferrer University of Texas Health Science Center at San Antonio 79 PUBLICATIONS 2,097 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Small Troubles, Adaptive Responses View project All content following this page was uploaded by Robert Ferrer on 06 January 2014. The user has requested enhancement of the downloaded file. Vol. 30, No. 8 579 Research Series Graphical Methods for Detecting Bias in Meta-analysis Robert L. Ferrer, MD, MPH The trustworthiness of meta-analysis, a set of techniques used to quantitatively combine results from different studies, has recently been questioned. Problems with meta-analysis stem from bias in selecting studies to include in a meta-analysis and from combining study results when it is inappro- priate to do so. Simple graphical techniques address these problems but are infrequently applied. Funnel plots display the relationship of effect size versus sample size and help determine whether there is likely to have been selection bias in including studies in the meta-analysis. The L’Abbé plot displays the outcomes in both the treatment and control groups of included studies and helps to decide whether the studies are too heterogeneous to appropriately combine into a single measure of effect. (Fam Med 1998;30(8):579-83.) Our faith in the answers provided by scientific in- multiple studies to see the patterns that clarify a line quiry rests on our confidence that its methods are of scientific inquiry. As a way of summarizing re- sound. If we lose confidence in a particular method, search, the systematic review is considered superior we may begin to doubt a whole series of previously to the traditional expert narrative review,2 which is established “truths.” Such skepticism1 has arisen about vulnerable to the biases of its authors. meta-analysis, the “study of studies” designed to com- Enthusiasm for meta-analysis is based on the fact bine the results of a number of different reports into that it provides a quantitative synthesis of all the rel- one report to create a single, more precise estimate of evant and methodologically acceptable evidence.3 an effect. This article will briefly outline why skepti- This enthusiasm is reflected in the logarithmic growth cism has developed about meta-analysis and will of published meta-analyses, from 16 in all of the 1970s present two graphical methods to evaluate the valid- to more than 500 in 1996.4 The Cochrane Collabora- ity of a meta-analysis. tion, a worldwide effort to produce systematic reviews First developed by social scientists as a way to in- to guide evidence-based practice, further exemplifies tegrate and summarize evidence from multiple stud- a strong faith in the validity of meta-analysis. ies, meta-analysis forms the statistical core of the sci- Not everyone is so positive about meta-analysis, ence of systematic review. The central idea behind however. Critics have argued that attempting to sum- systematic reviews is that no single study can pro- marize the results of different studies with a single vide a definitive answer to a medical question. Rather, measure leads to a substantial risk of error,5-7 and progressive understanding develops through a pro- empirical evidence has recently emerged to support cess of synthesizing and integrating the results from their doubts. Several studies have demonstrated dis- cordant results in 10%–34% of comparisons between meta-analyses and large randomized clinical trials (RCTs) on the same topic.8-10 Though there is no a From the Department of Family Practice, University of Texas Health priori basis for believing that RCTs are always cor- Science Center at San Antonio. rect when a discrepancy exists, the 50-year history of 580 September 1998 Family Medicine building knowledge with RCTs has weighed in their favor, and the validity of meta-analyses has become suspect. Table 1 Sources of Bias in Meta-analysis Bias in Meta-analysis The most important biases in meta-analysis arise Type of Bias Definition from two sources. These are 1) the choice of studies Publication bias20 Positive studies* are submitted for 11 publication more frequently and are more included in the meta-analysis and 2) how the results easily published than negative studies.** of those studies are combined to produce a summary effect estimate.7 Multiple publication21 Duplicate publication of study results leads to over-sampling of data from the Just as bias in selecting patients may flaw an epi- same research. demiological study, bias in selecting studies may flaw a meta-analysis. Selection bias in meta-analysis de- English-language bias22 Negative studies are less likely to be published in English-language journals rives from several potential problems with literature than positive studies. Thus, English- searches,11 including publication bias, citation bias, language literature searches fail to retrieve multiple publication, and English-language bias (see negative studies. Table 1 for definitions). Each of these biases increases Citation bias23 Positive studies are cited more frequently the likelihood that meta-analysis will include studies and thus are more easily identified than that demonstrate statistically significant differences negative studies. and fail to include those that do not. * Positive studies—studies that demonstrate statistically significant The other major bias occurs when individual stud- differences ies are combined despite significant heterogeneity in ** Negative studies—studies that do not demonstrate statistically significant differences their results. In such cases, summary measures are misleading and can mask impor- tant subgroup differences in out- come. For instance, variation in treatment effect by patient age Figure 1 would be hidden by an analysis that pooled the results of studies Symmetrical Funnel Plot done in young and old popula- tions. Or, the existence of a threshold dose would be ob- scured by an analysis that com- bined low-dose and high-dose treatment studies. Unfortunately, most meta- analyses are not presented in a way that permits readers to assess whether biases are present, even though there are simple graphi- cal methods that help to do so. These graphical methods include funnel plots and L’Abbé plots. The Funnel Plot The funnel plot is a simple vi- sual tool to examine whether a meta-analysis is based on a bi- ased sample of studies.12 The fun- nel plot is a scatterplot of effect size versus sample includes an unbiased sample of studies, including size, with each data point on the plot representing one studies that find both positive and negative results, study. Because large studies estimate effect size more the funnel is symmetrical. Figure 1 shows a schematic precisely than small studies, they tend to lie in a nar- representation of a symmetrical funnel plot. row band at the top of the scatterplot, while the smaller When the sample of studies is biased, however, the studies, with more variation in results, fan out over a funnel will be asymmetrical. This asymmetry is most larger area at the bottom, thus creating the visual im- commonly the result of smaller studies being biased pression of an inverted funnel. When a meta-analysis toward positive results and larger effect sizes, due to Research Series Vol. 30, No. 8 581 the biases listed in Table 1. Fig- ure 2 shows a funnel plot cre- Figure 2 ated from a meta-analysisl3 that found that calcium supplemen- Asymmetrical Funnel Plot: Meta-analysis of Calcium and Preeclampsia tation during pregnancy was as- sociated with a lower risk of preeclampsia. Marked asymme- try in the funnel plot suggests bi- ased inclusion of studies that showed beneficial effects from calcium. In fact, 15 months af- ter the publication of that meta- analysis, a large randomized trial14 found no benefit from cal- cium supplementation in pre- venting preeclampsia. In many cases, visually in- specting a funnel plot suffices to draw conclusions about possible bias. But, if uncertainty persists about its symmetry, statistical techniques are available to test the hypothesis that a funnel plot is symmetrical.12 Funnel plots may also be used to examine whether a line of in- Figure 3 quiry is converging on a more precise effect estimate over Funnel Plot: Study Result Versus Year of Publication time.15 In this case, effect esti- mate is plotted against year of publication. The schematic ex- ample in Figure 3 shows that the most recent studies have less variability in their results than older studies, suggesting that current research methods are providing more accurate results than older methods. The L’Abbé Plot The L’Abbé plot16 (pro- nounced lah-bay) is another type of scatterplot in which each data point represents one study. The event rate in the control group is plotted against the event rate in the treatment group (the event rate can be either the proportion or the rate at which the endpoint occurred in each group). If the treatment group has better outcomes than the control group, data points fall below the line of identity, which has a slope = 1. If the control has better outcomes than the treatment, the data points fall above the line of identity (Figure 4). The L’Abbé plot best addresses this question about a meta-analysis: how does the treatment effect relate 582 September 1998 Family Medicine to baseline risk? If the relation- ship is similar from study to Figure 4 study, the data points will form a consistent band on the L’Abbé Plot scatterplot, as in the schematic L’Abbé plot in Figure 5, and it is appropriate to calculate a summary effect measure.
Load more

Recommended publications
  • Science for Judges Ii
    BERGERINTRO.DOC 4/23/2004 12:32 PM SCIENCE FOR JUDGES II INTRODUCTION Margaret A. Berger* This issue of the Journal of Law and Policy contains a second installment of articles about science-related questions that arise in the litigation context. As previously explained, these essays are expanded and edited versions of presentations made to federal and state judges at programs funded by the Common Benefit Trust established in the Silicone Breast Implant Products Liability Litigation.1 These conferences are held at Brooklyn Law School under the auspices of its Center for Health Law and Policy, in collaboration with the Federal Judicial Center, the National Center for State Courts, and the National Academies of Science=s Panel on Science, Technology and Law. Science for Judges II focused on two principal topics: (1) the practice of epidemiology and its role in judicial proceedings; and (2) the production of science through the regulatory process of administrative agencies. Epidemiology has played a significant role in toxic tort actions in proving causation, often the most crucial issue in dispute. A failure to prove causation means a victory for the defense. Many courts consider epidemiologic evidence the “gold standard” of proof, and some judges go so far as to hold that a plaintiff cannot prevail in proving causation in the absence of confirmatory epidemiologic studies.2 The three papers on epidemiology by * Suzanne J. and Norman Miles Professor of Law, Brooklyn Law School. Professor Berger is the Director of the Science for Judges Program. 1 See Margaret A. Berger, Introduction, Science for Judges, 12 J.
    [Show full text]
  • Methodological Challenges Associated with Meta-Analyses in Health Care and Behavioral Health Research
    o title: Methodological Challenges Associated with Meta Analyses in Health Care and Behavioral Health Research o date: May 14, 2012 Methodologicalo author: Challenges Associated with Judith A. Shinogle, PhD, MSc. Meta-AnalysesSenior Research Scientist in Health , Maryland InstituteCare for and Policy Analysis Behavioraland Research Health Research University of Maryland, Baltimore County 1000 Hilltop Circle Baltimore, Maryland 21250 http://www.umbc.edu/mipar/shinogle.php Judith A. Shinogle, PhD, MSc Senior Research Scientist Maryland Institute for Policy Analysis and Research University of Maryland, Baltimore County 1000 Hilltop Circle Baltimore, Maryland 21250 http://www.umbc.edu/mipar/shinogle.php Please see http://www.umbc.edu/mipar/shinogle.php for information about the Judith A. Shinogle Memorial Fund, Baltimore, MD, and the AKC Canine Health Foundation (www.akcchf.org), Raleigh, NC. Inquiries may also be directed to Sara Radcliffe, Executive Vice President for Health, Biotechnology Industry Organization, www.bio.org, 202-962-9200. May 14, 2012 Methodological Challenges Associated with Meta-Analyses in Health Care and Behavioral Health Research Judith A. Shinogle, PhD, MSc I. Executive Summary Meta-analysis is used to inform a wide array of questions ranging from pharmaceutical safety to the relative effectiveness of different medical interventions. Meta-analysis also can be used to generate new hypotheses and reflect on the nature and possible causes of heterogeneity between studies. The wide range of applications has led to an increase in use of meta-analysis. When skillfully conducted, meta-analysis is one way researchers can combine and evaluate different bodies of research to determine the answers to research questions. However, as use of meta-analysis grows, it is imperative that the proper methods are used in order to draw meaningful conclusions from these analyses.
    [Show full text]
  • Warwick.Ac.Uk/Lib-Publications
    A Thesis Submitted for the Degree of PhD at the University of Warwick Permanent WRAP URL: http://wrap.warwick.ac.uk/87969 Copyright and reuse: This thesis is made available online and is protected by original copyright. Please scroll down to view the document itself. Please refer to the repository record for this item for information to help you to cite it. Our policy information is available from the repository home page. For more information, please contact the WRAP Team at: [email protected] warwick.ac.uk/lib-publications An exploration of the construct of psychopathy, its measurement and neuropsychological correlates Gary Burgess This thesis is submitted in partial fulfilment of the requirements for the degree of Doctorate in Clinical Psychology Coventry University, Faculty of Health and Life Sciences University of Warwick, Department of Psychology May 2016 i Contents v Chapter 1; Literature Review 1 1.0 Abstract ........................................................................................................................................................ 2 Aim 2 Method 2 Findings 2 Conclusion 3 1.1 Introduction............................................................................................................................................... 4 1.1.2 Structure and Function of the Brain in Psychopathy 5 1.1. 3 Neuropsychological and Neurocognitive Approach 9 1.1.4 Subtypes 10 1.1.5 Factor structure models of psychopathy 11 1.1.6 Aims of Present Review 15 1.2 Method ......................................................................................................................................................
    [Show full text]
  • Publication Bias
    CHAPTER 30 Publication Bias Introduction The problem of missing studies Methods for addressing bias Illustrative example The model Getting a sense of the data Is there evidence of any bias? Is the entire effect an artifact of bias? How much of an impact might the bias have? Summary of the findings for the illustrative example Some important caveats Small-study effects Concluding remarks INTRODUCTION While a meta-analysis will yield a mathematically accurate synthesis of the studies included in the analysis, if these studies are a biased sample of all relevant studies, then the mean effect computed by the meta-analysis will reflect this bias. Several lines of evidence show that studies that report relatively high effect sizes are more likely to be published than studies that report lower effect sizes. Since published studies are more likely to find their way into a meta-analysis, any bias in the literature is likely to be reflected in the meta-analysis as well. This issue is generally known as publication bias. The problem of publication bias is not unique to systematic reviews. It affects the researcher who writes a narrative review and even the clinician who is searching a database for primary papers. Nevertheless, it has received more attention with regard to systematic reviews and meta-analyses, possibly because these are pro- moted as being more accurate than other approaches to synthesizing research. In this chapter we first discuss the reasons for publication bias and the evidence that it exists. Then we discuss a series of methods that have been developed to assess Introduction to Meta-Analysis.
    [Show full text]
  • Package 'Metaviz'
    Package ‘metaviz’ April 9, 2020 Type Package Title Forest Plots, Funnel Plots, and Visual Funnel Plot Inference for Meta-Analysis Version 0.3.1 Description A compilation of functions to create visually appealing and information-rich plots of meta-analytic data using 'ggplot2'. Currently allows to create forest plots, funnel plots, and many of their variants, such as rainforest plots, thick forest plots, additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities for visual inference with the funnel plot in the context of meta-analysis are provided. License GPL-2 Encoding UTF-8 LazyData true Imports ggplot2 (>= 3.1.0), nullabor (>= 0.3.5), dplyr (>= 0.7.8), RColorBrewer (>= 1.1-2), metafor (>= 1.9-9), gridExtra (>= 2.2.1), ggpubr (>= 0.1.6) Suggests Cairo (>= 1.5-9), knitr, rmarkdown Depends R (>= 3.2.0) RoxygenNote 7.1.0 VignetteBuilder knitr URL https://github.com/Mkossmeier/metaviz BugReports https://github.com/Mkossmeier/metaviz/issues NeedsCompilation no Author Michael Kossmeier [cre, aut], Ulrich S. Tran [aut], Martin Voracek [aut] Maintainer Michael Kossmeier <[email protected]> Repository CRAN Date/Publication 2020-04-09 09:10:08 UTC 1 2 metaviz-package R topics documented: metaviz-package . .2 brainvol . .3 exrehab . .4 funnelinf . .5 homeopath . .8 mozart . .9 viz_forest . 10 viz_funnel . 13 viz_rainforest . 17 viz_sunset . 21 viz_thickforest . 24 Index 28 metaviz-package metaviz: Forest Plots, Funnel Plots, and Visual Funnel Plot Inference for Meta-Analysis Description The package metaviz is a collection of functions to create visually appealing and information- rich plots of meta-analytic data using ggplot2.
    [Show full text]
  • Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: a Practical Review for Clinical Researchers–Part I
    Review Article | Experimental and Others http://dx.doi.org/10.3348/kjr.2015.16.6.1175 pISSN 1229-6929 · eISSN 2005-8330 Korean J Radiol 2015;16(6):1175-1187 Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers–Part I. General Guidance and Tips Kyung Won Kim, MD, PhD1*, Juneyoung Lee, PhD2*, Sang Hyun Choi, MD1, Jimi Huh, MD1, Seong Ho Park, MD, PhD1 1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea; 2Department of Biostatistics, Korea University College of Medicine, Seoul 02841, Korea In the field of diagnostic test accuracy (DTA), the use of systematic review and meta-analyses is steadily increasing. By means of objective evaluation of all available primary studies, these two processes generate an evidence-based systematic summary regarding a specific research topic. The methodology for systematic review and meta-analysis in DTA studies differs from that in therapeutic/interventional studies, and its content is still evolving. Here we review the overall process from a practical standpoint, which may serve as a reference for those who implement these methods. Index terms: Systematic review; Meta-analysis; Diagnostic test accuracy INTRODUCTION select, and critically appraise relevant research and to analyze data from the primary studies included in the With the continued publication of primary scientific review (1). As a research synthesis methodology, systematic research studies and the recognition of their importance, review can evaluate a body of evidence in the literature the value of systematic reviews and meta-analyses both qualitatively and quantitatively.
    [Show full text]
  • Testing for Funnel Plot Asymmetry of Standardized Mean Differences
    Running head: FUNNEL PLOT ASYMMETRY OF SMD 1 Testing for funnel plot asymmetry of standardized mean differences James E. Pustejovsky1 & Melissa Rodgers1 1 The University of Texas at Austin Author Note An earlier version of this paper was presented at the 2018 annual convention of the American Educational Research Association in New York, NY. The authors gratefully acknowledge critical feedback from Dr. Betsy Becker. Supplementary materials for this article are available on the Open Science Framework at https://osf.io/4ntcy/. Correspondence concerning this article should be addressed to James E. Pustejovsky, The University of Texas at Austin, Educational Psychology Department, 1912 Speedway, MS D5800, Austin, TX 78712. E-mail: [email protected] FUNNEL PLOT ASYMMETRY OF SMD 2 Abstract Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size estimates, including several methods based on assessment of asymmetry of funnel plots, such as Egger’s regression test, the rank correlation test, and the Trim-and-Fill test. Previous research has demonstated that Egger’s regression test is mis-calibrated when applied to log-odds ratio effect size estimates, due to artifactual correlation between the effect size estimate and its standard error. This study examines similar problems that occur in meta-analyses of the standardized mean difference, a ubiquitous effect size measure in educational and psychological research. In a simulation study of standardized mean difference effect sizes, we assess the Type I error rates of conventional tests of funnel plot asymmetry, as well as the likelihood ratio test from a three-parameter selection model.
    [Show full text]
  • Thresholds for Statistical and Clinical Significance in Systematic Reviews with Meta-Analytic Methods
    Jakobsen et al. BMC Medical Research Methodology 2014, 14:120 http://www.biomedcentral.com/1471-2288/14/120 CORRESPONDENCE Open Access Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods Janus Christian Jakobsen1,2*, Jørn Wetterslev1, Per Winkel1, Theis Lange3 and Christian Gluud1 Abstract Background: Thresholds for statistical significance when assessing meta-analysis results are being insufficiently demonstrated by traditional 95% confidence intervals and P-values. Assessment of intervention effects in systematic reviews with meta-analysis deserves greater rigour. Methods: Methodologies for assessing statistical and clinical significance of intervention effects in systematic reviews were considered. Balancing simplicity and comprehensiveness, an operational procedure was developed, based mainly on The Cochrane Collaboration methodology and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines. Results: We propose an eight-step procedure for better validation of meta-analytic results in systematic reviews (1) Obtain the 95% confidence intervals and the P-values from both fixed-effect and random-effects meta-analyses and report the most conservative results as the main results. (2) Explore the reasons behind substantial statistical heterogeneity using subgroup and sensitivity analyses (see step 6). (3) To take account of problems with multiplicity adjust the thresholds for sig- nificance according to the number of primary outcomes. (4) Calculate required information sizes (≈ the apriorirequired number of participants for a meta-analysis to be conclusive) for all outcomes and analyse each outcome with trial sequen- tial analysis. Report whether the trial sequential monitoring boundaries for benefit, harm, or futility are crossed. (5) Calculate Bayes factors for all primary outcomes.
    [Show full text]
  • 2010 SREE Conference Abstract Template
    Abstract Title Page Title: Investigating the File Drawer Problem in Causal Effects Studies in Science Education Authors and Affiliations: Joseph Taylor Director of Research and Development BSCS 719.219.4104 [email protected] • www.bscs.org Susan Kowalski, BSCS Molly Stuhlsatz, BSCS Christopher Wilson, BSCS Jessaca Spybrook, Western Michigan University Preferred Sections 1. Mathematics and Science Education in Secondary Grades 2. Research Methods SREE Fall 2013 Conference Abstract Template Abstract Body Background / Context: This paper seeks to investigate the existence of a specific type of publication bias. That is, the bias that can result when causal effect studies with small sample sizes (low statistical power) yield small effects that are not statistically significant and therefore not submitted or accepted for publication. In other words, we are investigating for science education studies what Robert Rosenthal originally coined as the “file drawer” problem (Rosenthal, 1979). The importance of this work is highlighted by two specific problems that can result from this type of publication bias. First and foremost, the field loses the benefit of learning about interventions that may have a small or moderate effect, but an important effect nonetheless. Further, the field also benefits from learning about interventions had a near-zero or a negative effect in a particular study context. Second, this type of publication bias poses threats to validity for meta-analyses. Specifically, when meta-analysts compute summary effect sizes for a particular group of studies, it is assumed that the weighted mean summary effect is based on a representative sample of all of the effects available for the intervention in question (Borenstein, 2009).
    [Show full text]
  • Graphical Analysis for Epidemiology, Manufacturing and Marketing Applications
    Graphical Analysis for Epidemiology, Manufacturing and Marketing Applications Andrew Berridge, Michael O’Connell TIBCO Data Science © Copyright 2000-2015 TIBCO Software Inc. Agenda Graphical Analysis for Epidemiology, Manufacturing and Marketing • Epidemiology - meta analysis, with: • Forest Plots • Double-dot Plots • Funnel Plots • Manufacturing • Control Charts • Root Cause Analysis • Operator, Supervisor Dashboards, predicting equipment failure • Sales and Marketing • Infographics • Trend Analysis • Field-force Optimisation 2 © Copyright 2000-2015 TIBCO Software Inc. Epidemiology – Forest Plot Forest Plot • Most common graphical display for presenting results from meta-analyses • Effect estimates displayed with their confidence intervals • Area of symbol proportional to weight of estimate • Larger symbol draws attention to studies with larger weight • i.e. with smaller confidence intervals • Any type of effect estimate can be used: • Odds ratios, risk ratios, hazard ratios, mean differences, etc. 3 © Copyright 2000-2015 TIBCO Software Inc. Epidemiology – Forest Plot • Example showing meta-analysis of Risk Ratio of cardiovascular events • Comparing: • Individuals having one or more copies of any CYP2C19 genetic variant associated with reduced enzyme function • With individuals having none of these • Higher risk ratio corresponds to higher chance of cardiovascular events • Ordered by the weight that they have in the meta-analysis 4 © Copyright 2000-2015 TIBCO Software Inc. Epidemiology – Forest Plot 5 © Copyright 2000-2015 TIBCO Software Inc. Epidemiology – Double Dot Plot Forest Plot vs Double Dot Plot • Forest Plot • No interpretation of result on natural scale of risk • Double Dot Plot adds raw risks in each study to Forest Plot in an additional panel 6 © Copyright 2000-2015 TIBCO Software Inc. Epidemiology – Double Dot Plot (AE Example) 7 © Copyright 2000-2015 TIBCO Software Inc.
    [Show full text]
  • (Sensitivity Analysis Excluding Three Studies). A. Forest Plot (REM) and B
    1 Appendix: Figure S1: Meta-analysis (sensitivity analysis excluding three studies). A. Forest plot (REM) and B. Forest plot (FEM) comparing the mean differences in calprotectin level between severe and non-severe COVID-19. Studies 1-5 are respectively - De Guadiana et al [25]; Shi et al [26]; Silvin et al [12]; Bauer et al [27]; Ojetti et al [28]. C. Funnel plot (for the sensitivity analysis excluding three studies) shows no publication bias, an improvement from the total cohort funnel plot shown in figure 3c. D. Funnel plot (Subgroup analysis - Serum group) shows no publication bias. E. Funnel plot (Subgroup analysis - faecal group) shows some evidence of publication bias with much asymmetry. RAPHAEL UDEH, c3197610, PUBH6303 2 Table S1: Quality assessment for the included cohort / case-control studies using Newcastle- 31-32 Ottawa Scale Study Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Score Chen et al14 * * * * * * * * * 9/9 Shi et al26 * * * * * * * * 8/9 Silvin et al12 * * * * ** * * * 9/9 Bauer et al27 * * * * * * * * 8/9 Effenberger * * * * * * * NR 7/9 et al18 Ojetti et al28 * * * * * * * * 8/9 Britton et * * * * * * * * 8/9 al29 Unterman et * * * * * * * * 8/9 al33 Livanos et * * - - - * * NR 4/9 al34 NB: Items were as follows for cohort studies: 1-representativeness of the exposed cohort; 2- selection of the nonexposed cohort; 3-ascertainment of exposure; 4-demonstration that the outcome of interest was not present at the start of the study; **5-a comparability of cohorts on the basis of the design or analysis; 6-assessment of the outcome 7-follow-up period was long enough for outcomes to occur; 8-adequacy of follow-up evaluation (>75% follow-up evaluation, or description for those lost).
    [Show full text]
  • Neighbourhood Socioeconomic Status and Overweight/Obesity: a Systematic Review and Meta-­Analysis of Epidemiological Studies
    Open access Original research BMJ Open: first published as 10.1136/bmjopen-2018-028238 on 14 November 2019. Downloaded from Neighbourhood socioeconomic status and overweight/obesity: a systematic review and meta- analysis of epidemiological studies Shimels Hussien Mohammed ,1 Tesfa Dejenie Habtewold ,2,3 Mulugeta Molla Birhanu,4 Tesfamichael Awoke Sissay,5 Balewgizie Sileshi Tegegne,2 Samer Abuzerr,6 Ahmad Esmaillzadeh1,7,8 To cite: Mohammed SH, ABSTRACT Strengths and limitations of this study Habtewold TD, Birhanu MM, Objective Low neighbourhood socioeconomic status et al. Neighbourhood (NSES) has been linked to a higher risk of overweight/ socioeconomic status ► This is the first meta-analysis study on the associ- and overweight/obesity: a obesity, irrespective of the individual’s own socioeconomic ation of neighbourhood socioeconomic status with systematic review and meta- status. No meta- analysis study has been done on the overweight/obesity. analysis of epidemiological association. Thus, this study was done to synthesise ► The report is based on a large number of studies, studies. BMJ Open the existing evidence on the association of NSES with covering over a million individuals, which improves 2019;9:e028238. doi:10.1136/ overweight, obesity and body mass index (BMI). the representativeness of the sample. bmjopen-2018-028238 Design Systematic review and meta- analysis. ► The studies included in this work are observational ► Prepublication history and Data sources PubMed, Embase, Scopus, Cochrane in design, precluding making causal inference. additional material for this Library, Web of Sciences and Google Scholar databases ► The study shares the limitations of ecological paper are available online. To were searched for articles published until 25 September studies.
    [Show full text]