A Tutorial for Meta-Analysis of Diagnostic Tests for Low- Prevalence Diseases: Bayesian Models and Software
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The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution
1BRIEF METHOD NOTE 2The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, 3Evolution and Beyond 4Shinichi Nakagawa1, Malgorzata Lagisz1,5, Rose E. O’Dea1, Joanna Rutkowska2, Yefeng Yang1, 5Daniel W. A. Noble3,5, and Alistair M. Senior4,5. 61 Evolution & Ecology Research Centre and School of Biological, Earth and Environmental 7Sciences, University of New South Wales, Sydney, NSW 2052, Australia 82 Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland 93 Division of Ecology and Evolution, Research School of Biology, The Australian National 10University, Canberra, ACT, Australia 114 Charles Perkins Centre and School of Life and Environmental Sciences, University of Sydney, 12Camperdown, NSW 2006, Australia 135 These authors contributed equally 14* Correspondence: S. Nakagawa & A. M. Senior 15e-mail: [email protected] 16email: [email protected] 17Short title: The Orchard Plot 18 1 19Abstract 20‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a 21meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect 22sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and 23evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, 24showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or 25categories in a meta-regression. We propose a modification of the forest-like plot, which we name 26the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta- 27analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes 28scaled by their precision. -
Statistical Evaluation of Diagnostic Tests – Part 2 [Pre-Test and Post-Test Probability and Odds, Likelihood Ratios, Receiver
86 Journal of The Association of Physicians of India ■ Vol. 65 ■ July 2017 STATISTICS FOR RESEARCHERS Statistical Evaluation of Diagnostic Tests – Part 2 [Pre-test and post-test probability and odds, Likelihood ratios, Receiver Operating Characteristic Curve, Youden’s Index and Diagnostic test biases] NJ Gogtay, UM Thatte Introduction Understanding Probability From the example, it follows that and Odds and the Odds = p/1-p, where p is the n the previous article on probability of the event occurring. the statistical evaluation Relationship between the I Probability, on the other hand, of diagnostic tests –Part 1, we Two is given by the formula understood the measures of sensitivity, specificity, positive Let us understand probability p = Odds/1+Odds and negative predictive values. and odds with the example of The use of these metrices stems a drug producing bleeding in Bayesian Statistics, Pre- from the fact that no diagnostic 10/100 patients treated with it. Test Probability and Pre- test is ever perfect and every time The probability of bleeding will Test Odds we carry out a test, it will yield be 10/100 [10%], while the odds of one of four possible outcomes– bleeding will be 10/90 [11%]. This A clinician often suspects that true positive, false positive, true is because odds is defined as the a patient has the disease even negative or false negative. The 2 x probability of the event occurring before he orders a test [screening 2 table [Table 1] gives each of these divided by the probability of or diagnostic] on the patient. For four possibilities along with their the event not occurring.2 Thus, example, when a patient who is mathematical calculations when a every odds can be expressed as a chronic smoker and presents new test is compared with a gold probability and every probability with cough and weight loss of a standard test.1 as odds as these are two ways of six-month duration, the suspicion In this article, the second in explaining the same concept. -
Interpreting Subgroups and the STAMPEDE Trial
“Thursday’s child has far to go” -- Interpreting subgroups and the STAMPEDE trial Melissa R Spears MRC Clinical Trials Unit at UCL, London Nicholas D James Institute of Cancer and Genomic Sciences, University of Birmingham Matthew R Sydes MRC Clinical Trials Unit at UCL STAMPEDE is a multi-arm multi-stage randomised controlled trial protocol, recruiting men with locally advanced or metastatic prostate cancer who are commencing long-term androgen deprivation therapy. Opening to recruitment with five research questions in 2005 and adding in a further five questions over the past six years, it has reported survival data on six of these ten RCT questions over the past two years. [1-3] Some of these results have been of practice-changing magnitude, [4, 5] but, in conversation, we have noticed some misinterpretation, both over-interpretation and under-interpretation, of subgroup analyses by the wider clinical community which could impact negatively on practice. We suspect, therefore, that such problems in interpretation may be common. Our intention here is to provide comment on interpretation of subgroup analysis in general using examples from STAMPEDE. Specifically we would like to highlight some possible implications from the misinterpretation of subgroups and how these might be avoided, particularly where these contravene the very design of the research question. In this, we would hope to contribute to the conversation on subgroup analyses. [6-11] For each comparison in STAMPEDE, or indeed virtually any trial, the interest is the effect of the research treatment under investigation on the primary outcome measure across the whole population. Upon reporting the primary outcome measure, the consistency of effect across pre-specified subgroups, including stratification factors at randomisation, is presented; these are planned analyses. -
The Diagnostic Odds Ratio: a Single Indicator of Test Performance Afina S
Journal of Clinical Epidemiology 56 (2003) 1129–1135 The diagnostic odds ratio: a single indicator of test performance Afina S. Glasa,*, Jeroen G. Lijmerb, Martin H. Prinsc, Gouke J. Bonseld, Patrick M.M. Bossuyta aDepartment of Clinical Epidemiology & Biostatistics, University of Amsterdam, Academic Medical Center, Post Office Box 22700, 100 DE Amsterdam, The Netherlands bDepartment of Psychiatry, University Medical Center, Post Office Box 85500, 3508 GA, Utrecht, The Netherlands cDepartment of Epidemiology, University of Maastricht, Post Office Box 6166200 MD, Maastricht, The Netherlands dDepartment of Public Health, Academic Medical Center, Post Office Box 22700, 1100 DE, Amsterdam, The Netherlands Accepted 17 April 2003 Abstract Diagnostic testing can be used to discriminate subjects with a target disorder from subjects without it. Several indicators of diagnostic performance have been proposed, such as sensitivity and specificity. Using paired indicators can be a disadvantage in comparing the performance of competing tests, especially if one test does not outperform the other on both indicators. Here we propose the use of the odds ratio as a single indicator of diagnostic performance. The diagnostic odds ratio is closely linked to existing indicators, it facilitates formal meta-analysis of studies on diagnostic test performance, and it is derived from logistic models, which allow for the inclusion of additional variables to correct for heterogeneity. A disadvantage is the impossibility of weighing the true positive and false positive rate separately. In this article the application of the diagnostic odds ratio in test evaluation is illustrated. Ć 2003 Elsevier Inc. All rights reserved. Keywords: Diagnostic odds ratio; Tutorial; Diagnostic test; Sensitivity and specificity; Logistic regression; Meta-analysis 1. -
Meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice
meta4diag: Bayesian Bivariate Meta-analysis of Diagnostic Test Studies for Routine Practice J. Guo and A. Riebler Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, PO 7491, Norway. July 8, 2016 Abstract This paper introduces the R package meta4diag for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package meta4diag is a purpose-built front end of the R package INLA. While INLA offers full Bayesian inference for the large set of latent Gaussian models using integrated nested Laplace approximations, meta4diag extracts the features needed for bivariate meta-analysis and presents them in an intuitive way. It allows the user a straightforward model- specification and offers user-specific prior distributions. Further, the newly proposed penalised complexity prior framework is supported, which builds on prior intuitions about the behaviours of the variance and correlation parameters. Accurate posterior marginal distributions for sensitivity and specificity as well as all hyperparameters, and covariates are directly obtained without Markov chain Monte Carlo sampling. Further, univariate estimates of interest, such as odds ratios, as well as the SROC curve and other common graphics are directly available for interpretation. An in- teractive graphical user interface provides the user with the full functionality of the package without requiring any R programming. The package is available through CRAN https://cran.r-project.org/web/packages/meta4diag/ and its usage will be illustrated using three real data examples. arXiv:1512.06220v2 [stat.AP] 7 Jul 2016 1 1 Introduction A meta-analysis summarises the results from multiple studies with the purpose of finding a general trend across the studies. -
09 May 2021 Aperto
AperTO - Archivio Istituzionale Open Access dell'Università di Torino How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study This is the author's manuscript Original Citation: Availability: This version is available http://hdl.handle.net/2318/1730744 since 2020-02-25T11:39:10Z Published version: DOI:10.1080/01431161.2020.1723173 Terms of use: Open Access Anyone can freely access the full text of works made available as "Open Access". Works made available under a Creative Commons license can be used according to the terms and conditions of said license. Use of all other works requires consent of the right holder (author or publisher) if not exempted from copyright protection by the applicable law. (Article begins on next page) 06 October 2021 International Journal of Remote Sensing and Remote Sensing Letters For Peer Review Only How far can We trust Forestry Estimates from Low Density LiDAR Acquisitions? The Cutfoot Sioux Experimental Forest (MN, USA) Case Study Journal: International Journal of Remote Sensing Manuscript ID TRES-PAP-2019-0377.R1 Manuscript Type: IJRS Research Paper Date Submitted by the 04-Oct-2019 Author: Complete List of Authors: Borgogno Mondino, Enrico; Universita degli Studi di Torino Dipartimento di Scienze Agrarie Forestali e Alimentari Fissore, Vanina; Universita degli Studi di Torino Dipartimento di Scienze Agrarie Forestali e Alimentari Falkowski, Michael; Colorado State University College of Agricultural Sciences Palik, Brian; USDA -
A Readers' Guide to the Interpretation of Diagnostic Test Properties
CORE Metadata, citation and similar papers at core.ac.uk Provided by RERO DOC Digital Library Intensive Care Med (2003) 29:1043–1051 DOI 10.1007/s00134-003-1761-8 REVIEW Joachim E. Fischer A readers’ guide to the interpretation Lucas M. Bachmann Roman Jaeschke of diagnostic test properties: clinical example of sepsis Received: 30 October 2002 Abstract Background: One of the hood ratios, pretest probability, post- Accepted: 13 March 2003 most challenging practical and daily test probability, and diagnostic odds Published online: 7 May 2003 problems in intensive care medicine ratio. Conclusions: We suggest the © Springer-Verlag 2003 is the interpretation of the results following minimal requirements for from diagnostic tests. In neonatology reporting on the diagnostic accuracy and pediatric intensive care the early of tests: a plot of the raw data, multi- diagnosis of potentially life-threaten- level likelihood ratios, the area under ing infections is a particularly impor- the receiver operating characteristic tant issue. Focus: A plethora of tests curve, and the cutoff yielding the J. E. Fischer (✉) have been suggested to improve di- highest discriminative ability. For Department of Pediatrics, agnostic decision making in the clin- critical appraisal it is mandatory to University Children’s Hospital, Steinweisstrasse 75, 8032 Zurich, ical setting of infection which is a report confidence intervals for each Switzerland clinical example used in this article. of these measures. Moreover, to al- e-mail: [email protected] Several criteria that are critical to ev- low comparison to the readers’ pa- Tel.: +41-1-2667751 idence-based appraisal of published tient population authors should pro- Fax: +41-1-2667164 data are often not adhered to during vide data on study population char- L. -
A Single Indicator of Test Performance E
UvA-DARE (Digital Academic Repository) Beyond diagnostic accuracy. Applying and extending methods for diagnostic test research Glas, A.S. Publication date 2003 Link to publication Citation for published version (APA): Glas, A. S. (2003). Beyond diagnostic accuracy. Applying and extending methods for diagnostic test research. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:30 Sep 2021 11 The Diagnostic Odds Ratio: A single indicator of test performance e Afinaa S. Glas, Jeroen G. Lijmer, Martin H. Prins, Gouke J. Bonsel, Patrickk M.M. Bossuyt Journall of Clinical Epidemiology, accepted Abstract t Diagnosticc testing can be used to discriminate subjects with a target disorder from subjectss without it. Several indicators of diagnostic performance have been proposed, suchh as sensitivity and specificity. -
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. -
Comprehensive Meta Analysis Version 2.0
Comprehensive Meta Analysis Version 2.0 This manual will continue to be revised to reflect changes in the program. It will also be expanded to include chapters covering conceptual topics. Upgrades to the program and manual will be available on our download site. 1 Comprehensive Meta Analysis Version 2 Developed by Michael Borenstein Larry Hedges Julian Higgins Hannah Rothstein Advisory group Doug Altman Betsy Becker Jesse Berlin Harris Cooper Despina Contopoulos-Ioannidis Kay Dickersin Sue Duval Matthias Egger Kim Goodwin Wayne Greenwood Julian Higgins John Ioannidis Spyros Konstantopoulos Mark Lipsey Michael McDaniel Fred Oswald Terri Pigott Stephen Senn Will Shadish Jonathan Sterne Alex Sutton Steven Tarlow Thomas Trikalinos Jeff Valentine John Vevea Vish Viswesvaran David Wilson This project was funded by the National Institutes of Health 2 Group meetings to develop the program July 2002. Left to right (Seated) Vish Viswesvaran, Will Shadish, Hannah Rothstein, Michael Borenstein, Fred Oswald, Terri Pigott. (Standing) Spyros Konstantopoulos, David Wilson, Alex Sutton, Jonathan Sterne, Harris Cooper, Sue Duval, Jesse Berlin, Larry Hedges, Mike McDaniel, Jack Vevea August, 2003. Left to right (Seated) David Wilson, Betsy Becker, Julian Higgins, Will Shadish, Hannah Rothstein, Michael Borenstein, Mike McDaniel, Steven Tarlow. (Standing) Spyros Konstantopoulos, Larry Hedges, Harris Cooper, John Ioannidis, Despina Contopoulos-Ioannidis, Jack Vevea, Sue Duval, Mark Lipsey, Alex Sutton, Terri Pigott, Fred Oswald, Wayne Greenwood, Thomas Trikalinos. 3 August, 2004 Left to right: Jonathan Sterne, Doug Altman, Alex Sutton, Michael Borenstein, Julian Higgins, Hannah Rothstein 4 Introduction The program installation will create a shortcut labeled Comprehensive Meta Analysis V2 on your desktop and also under “All programs” on the Windows Start menu. -
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. -
A Guide to Understanding Meta-Analysis
[ clinical commentary ] HEIDI ISRAEL, FNP, PhD1 • RANDY R. RICHTER, PT, PhD2 A Guide to Understanding Meta-Analysis eta-analysis is a popular and frequently used statistical able one to combine data and summarize technique used to combine data from several studies and the findings of several clinical trials that reexamine the effectiveness of treatment interventions. As evaluate the effectiveness or efficacy of a similar treatment approach on similar the number of articles using meta-analysis increases, under- M group of patients. This technique can standing of the benefits and drawbacks of the technique is essential. prove especially useful when there are several similar clinical trials with or with- Well-conducted systematic reviews of high level of evidence, and enhanced clin- out consistent outcomes, or when there randomized controlled trials are regarded ical interpretation of treatment effects, are smaller to medium-sized trials with as representing a high level of evidence.28 interpreting a meta-analysis is an impor- inconclusive results. Practicing in an evidence-based manner tant skill for physical therapists. The pur- By combining the results from 2 or is a recognized goal for the profession.3 pose of this commentary is to expand on more studies, a meta-analysis can in- Systematic reviews are used to answer existing articles describing meta-analysis crease statistical power18 and provide questions7,10,40 about the evidence sup- interpretation,6,13,14,42,61 discuss differences a single numerical value of the overall porting or refuting the effectiveness or in the results of a meta-analysis based on treatment effect. The meta-analysis result efficacy of an intervention.