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Journal articles on the topic 'Bayesian meta-analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Steel, Piers D. G., and John Kammeyer-Mueller. "Bayesian Variance Estimation for Meta-Analysis." Organizational Research Methods 11, no. 1 (2007): 54–78. http://dx.doi.org/10.1177/1094428107300339.

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2

Velanovich, V. "Meta-analysis for combining Bayesian probabilities." Medical Hypotheses 35, no. 3 (1991): 192–95. http://dx.doi.org/10.1016/0306-9877(91)90232-n.

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3

Brannick, Michael T., and Nanhua Zhang. "Bayesian meta-analysis of coefficient alpha." Research Synthesis Methods 4, no. 2 (2013): 198–207. http://dx.doi.org/10.1002/jrsm.1075.

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4

Karabatsos, George, Elizabeth Talbott, and Stephen G. Walker. "A Bayesian nonparametric meta-analysis model." Research Synthesis Methods 6, no. 1 (2014): 28–44. http://dx.doi.org/10.1002/jrsm.1117.

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5

Han, Hyemin. "A Novel Method to Use Coordinate Based Meta-Analysis to Determine a Prior Distribution for Voxelwise Bayesian Second-Level fMRI Analysis." Mathematics 10, no. 3 (2022): 356. http://dx.doi.org/10.3390/math10030356.

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Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study

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6

K. Hackenberger, Branimir. "Bayesian meta-analysis now – let's do it." Croatian Medical Journal 61, no. 6 (2020): 564–68. http://dx.doi.org/10.3325/cmj.2020.61.564.

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7

Bartoš, František, Quentin F. Gronau, Bram Timmers, Willem M. Otte, Alexander Ly, and Eric‐Jan Wagenmakers. "Bayesian model‐averaged meta‐analysis in medicine." Statistics in Medicine 40, no. 30 (2021): 6743–61. http://dx.doi.org/10.1002/sim.9170.

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8

Jang, Junghoon, Younshik Chung, Chansoo Kim, and Seongho Song. "Bayesian meta-analysis using skewed elliptical distributions." Journal of Statistical Computation and Simulation 79, no. 5 (2009): 691–704. http://dx.doi.org/10.1080/00949650801891595.

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9

Carter, George M., Debbie Indyk, Matthew Johnson, et al. "Micronutrients in HIV: A Bayesian Meta-Analysis." PLOS ONE 10, no. 4 (2015): e0120113. http://dx.doi.org/10.1371/journal.pone.0120113.

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10

Han, Hyemin, and Joonsuk Park. "Bayesian meta-analysis of fMRI image data." Cognitive Neuroscience 10, no. 2 (2019): 66–76. http://dx.doi.org/10.1080/17588928.2019.1570103.

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11

Schmid, Christopher H. "Using Bayesian Inference to Perform Meta-Analysis." Evaluation & the Health Professions 24, no. 2 (2001): 165–89. http://dx.doi.org/10.1177/01632780122034867.

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12

Bodnar, Olha, Rebecca Nalule Muhumuza, and Antonio Possolo. "Bayesian inference for heterogeneity in meta-analysis." Metrologia 57, no. 6 (2020): 064004. http://dx.doi.org/10.1088/1681-7575/abb064.

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13

Cong, Xiuyu, Dennis D. Cox, and Scott B. Cantor. "Bayesian meta-analysis of Papanicolaou smear accuracy." Gynecologic Oncology 107, no. 1 (2007): S133—S137. http://dx.doi.org/10.1016/j.ygyno.2007.08.080.

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14

Spence, Graeme T., David Steinsaltz, and Thomas R. Fanshawe. "A Bayesian approach to sequential meta‐analysis." Statistics in Medicine 35, no. 29 (2016): 5356–75. http://dx.doi.org/10.1002/sim.7052.

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15

Wei, Yinghui, and Julian P. T. Higgins. "Bayesian multivariate meta-analysis with multiple outcomes." Statistics in Medicine 32, no. 17 (2013): 2911–34. http://dx.doi.org/10.1002/sim.5745.

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16

Lin, Lifeng, and Haitao Chu. "Bayesian multivariate meta-analysis of multiple factors." Research Synthesis Methods 9, no. 2 (2018): 261–72. http://dx.doi.org/10.1002/jrsm.1293.

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17

Gronau, Quentin F., Daniel W. Heck, Sophie W. Berkhout, Julia M. Haaf, and Eric-Jan Wagenmakers. "A Primer on Bayesian Model-Averaged Meta-Analysis." Advances in Methods and Practices in Psychological Science 4, no. 3 (2021): 251524592110312. http://dx.doi.org/10.1177/25152459211031256.

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Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta-analysis models: (a) fixed-effect null hypothesis, (b) fixed-effect alternative hypothesis, (c) rand

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18

Neto, Ary Serpa, George Tomlinson, Sarina K. Sahetya, et al. "Higher PEEP for acute respiratory distress syndrome: a Bayesian meta-analysis of randomised clinical trials." Critical Care and Resuscitation 23, no. 2 (2021): 171–82. http://dx.doi.org/10.51893/2021.2.oa4.

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Objective: Benefit or harm of higher positive end expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) is controversial. We aimed to assess the impact of higher levels of PEEP in patients with ARDS under a Bayesian framework. Design: Systematic review and Bayesian meta-analysis of randomised clinical trials comparing higher to lower PEEP in adult patients with ARDS. Data sources: MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials from 1996 to 1 March 2020. Review methods: We extracted data from high quality randomised clinical trials comparing higher to low

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19

Mila, A. L., and H. K. Ngugi. "A Bayesian Approach to Meta-Analysis of Plant Pathology Studies." Phytopathology® 101, no. 1 (2011): 42–51. http://dx.doi.org/10.1094/phyto-03-10-0070.

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Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection produ

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20

Shim, Sung Ryul, Seong-Jang Kim, Jonghoo Lee, and Gerta Rücker. "Network meta-analysis: application and practice using R software." Epidemiology and Health 41 (April 8, 2019): e2019013. http://dx.doi.org/10.4178/epih.e2019013.

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The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for

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21

Chung, Younshik, Dipak Dey, and Junghoon Jang. "Semiparametric hierarchical selection models for bayesian meta analysis." Journal of Statistical Computation and Simulation 72, no. 10 (2002): 825–39. http://dx.doi.org/10.1080/00949650214672.

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22

Sutton, A. J., and K. R. Abrams. "Bayesian methods in meta-analysis and evidence synthesis." Statistical Methods in Medical Research 10, no. 4 (2001): 277–303. http://dx.doi.org/10.1191/096228001678227794.

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23

Carter, George M., Debbie Indyk, Matthew Johnson, et al. "Correction: Micronutrients in HIV: A Bayesian Meta-Analysis." PLOS ONE 11, no. 1 (2016): e0148392. http://dx.doi.org/10.1371/journal.pone.0148392.

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24

Jahan, Farzana, Earl W. Duncan, Susanna M. Cramb, Peter D. Baade, and Kerrie L. Mengersen. "Augmenting disease maps: a Bayesian meta-analysis approach." Royal Society Open Science 7, no. 8 (2020): 192151. http://dx.doi.org/10.1098/rsos.192151.

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Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an

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25

Sutton, Alex J., and Keith R. Abrams. "Bayesian methods in meta-analysis and evidence synthesis." Statistical Methods in Medical Research 10, no. 4 (2001): 277–303. http://dx.doi.org/10.1177/096228020101000404.

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26

Berry, Donald, J. Kyle Wathen, and Margaret Newell. "Bayesian model averaging in meta-analysis: Authors' response." Clinical Trials: Journal of the Society for Clinical Trials 6, no. 1 (2009): 50–51. http://dx.doi.org/10.1177/1740774509103252.

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27

Newcombe, Paul J., Claudio Verzilli, Juan P. Casas, Aroon D. Hingorani, Liam Smeeth, and John C. Whittaker. "Multilocus Bayesian Meta-Analysis of Gene-Disease Associations." American Journal of Human Genetics 84, no. 5 (2009): 567–80. http://dx.doi.org/10.1016/j.ajhg.2009.04.001.

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28

WATANABE, HIROSHI. "A Bayesian meta-analysis for reviewing effect sizes." Japanese Psychological Research 35, no. 3 (1993): 153–56. http://dx.doi.org/10.4992/psycholres1954.35.153.

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29

Moreno, E., F. J. Vázquez-Polo, and M. A. Negrín. "Objective Bayesian meta-analysis for sparse discrete data." Statistics in Medicine 33, no. 21 (2014): 3676–92. http://dx.doi.org/10.1002/sim.6163.

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30

Siegel, Lianne, Kyle Rudser, Siobhan Sutcliffe, et al. "A Bayesian multivariate meta‐analysis of prevalence data." Statistics in Medicine 39, no. 23 (2020): 3105–19. http://dx.doi.org/10.1002/sim.8593.

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31

Larose, Daniel T., and Dipak K. Dey. "Grouped random effects models for Bayesian meta-analysis." Statistics in Medicine 16, no. 16 (1997): 1817–29. http://dx.doi.org/10.1002/(sici)1097-0258(19970830)16:16<1817::aid-sim621>3.0.co;2-n.

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32

Abrams, Keith, and Bruno Sansó. "Approximate Bayesian inference for random effects meta-analysis." Statistics in Medicine 17, no. 2 (1998): 201–18. http://dx.doi.org/10.1002/(sici)1097-0258(19980130)17:2<201::aid-sim736>3.0.co;2-9.

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33

De Iorio, Maria, Paul J. Newcombe, Ioanna Tachmazidou, Claudio J. Verzilli, and John C. Whittaker. "Bayesian semiparametric meta-analysis for genetic association studies." Genetic Epidemiology 35, no. 5 (2011): 333–40. http://dx.doi.org/10.1002/gepi.20581.

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34

Yang, Yang, Jiaomiao Pei, Guozhen Gao, et al. "Pharmacological interventions for melanoma: Comparative analysis using bayesian meta-analysis." Oncotarget 7, no. 49 (2016): 80855–71. http://dx.doi.org/10.18632/oncotarget.12644.

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35

Odani, Motoi, Satoru Fukimbara, and Tosiya Sato. "A Bayesian meta-analytic approach for safety signal detection in randomized clinical trials." Clinical Trials 14, no. 2 (2017): 192–200. http://dx.doi.org/10.1177/1740774516683920.

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Background/Aim: Meta-analyses are frequently performed on adverse event data and are primarily used for improving statistical power to detect safety signals. However, in the evaluation of drug safety for New Drug Applications, simple pooling of adverse event data from multiple clinical trials is still commonly used. We sought to propose a new Bayesian hierarchical meta-analytic approach based on consideration of a hierarchical structure of reported individual adverse event data from multiple randomized clinical trials. Methods: To develop our meta-analysis model, we extended an existing three-

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36

Kruschke, John K., and Torrin M. Liddell. "The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective." Psychonomic Bulletin & Review 25, no. 1 (2017): 178–206. http://dx.doi.org/10.3758/s13423-016-1221-4.

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37

Varnas, Saulius, Xiaomeng Gu, and Andrew Metcalfe. "Bayesian Meta-Analysis of Myopia Control with Multifocal Lenses." Journal of Clinical Medicine 10, no. 4 (2021): 730. http://dx.doi.org/10.3390/jcm10040730.

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The aim of this study is to provide reliable guidelines for the mean percentage efficacy together with the 95% credibility interval in slowing down progression of myopia by a specific intervention over defined time periods, derived from a substantial number of randomised controlled clinical trials (RCTs) with consistent outcomes. Multifocal spectacles and contact lenses represent interventions with the largest number of RCTs carried out. Our meta-analyses considered 10 RCTs involving 1662 children which have tested the efficacy of progressive addition spectacle lenses (PALs). In a separate mod

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38

AVCI, Esin. "Using Informative Prior from Meta-Analysis in Bayesian Approach." Journal of Data Science 15, no. 4 (2021): 575–88. http://dx.doi.org/10.6339/jds.201710_15(4).00001.

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39

AVCI, Esin. "Bayesian Meta-Analysis of Prevalence: Alzheimer's Disease in Europe." Turkiye Klinikleri Journal of Biostatistics 10, no. 3 (2018): 208–14. http://dx.doi.org/10.5336/biostatic.2018-62212.

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40

dos Reis, B. R., S. Sujani, and R. R. White. "59. Additive bayesian network for systems-oriented meta-analysis." Animal - science proceedings 13, no. 4 (2022): 580–81. http://dx.doi.org/10.1016/j.anscip.2022.07.450.

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41

Migliorini, Filippo, Nicola Maffulli, Jörg Eschweiler, Matthias Knobe, Markus Tingart, and Giorgia Colarossi. "Pharmacological management of fibromyalgia: a Bayesian network meta-analysis." Expert Review of Clinical Pharmacology 15, no. 2 (2022): 205–14. http://dx.doi.org/10.1080/17512433.2022.2044792.

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42

الطيب, لبني. "CASE-BY-CASE META-ANALYSIS FROM A BAYESIAN PERSPECTIVE." المجلة العملیة التجارة والتمویل 41, no. 3 (2021): 114–31. http://dx.doi.org/10.21608/caf.2021.199349.

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43

Waidyatillake, Nilakshi T., Patricia T. Campbell, Don Vicendese, Shyamali C. Dharmage, Ariadna Curto, and Mark Stevenson. "Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis." International Journal of Environmental Research and Public Health 18, no. 14 (2021): 7655. http://dx.doi.org/10.3390/ijerph18147655.

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Background: We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies. Methods: The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the he

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44

Rogatko, André. "Bayesian approach for meta - analysis of controlled clinical trials." Communications in Statistics - Theory and Methods 21, no. 5 (1992): 1441–62. http://dx.doi.org/10.1080/03610929208830857.

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45

Burr, Deborah, and Hani Doss. "A Bayesian Semiparametric Model for Random-Effects Meta-Analysis." Journal of the American Statistical Association 100, no. 469 (2005): 242–51. http://dx.doi.org/10.1198/016214504000001024.

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46

Pappalardo, Paula, Kiona Ogle, Elizabeth A. Hamman, James R. Bence, Bruce A. Hungate, and Craig W. Osenberg. "Comparing traditional and Bayesian approaches to ecological meta‐analysis." Methods in Ecology and Evolution 11, no. 10 (2020): 1286–95. http://dx.doi.org/10.1111/2041-210x.13445.

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47

Pace, Nathan Leon. "BAYESIAN META-ANALYSIS AND THE PREVENTION OF SUCCINYLCHOLINE MYALGIAS." Anesthesiology 77, Supplement (1992): A1129. http://dx.doi.org/10.1097/00000542-199209001-01129.

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48

Garcia, A., M. J. Ouwens, M. Postma, H. A. Pham, and B. Heeg. "PCN409 BAYESIAN NETWORK META-ANALYSIS FOR MIXTURE CURE MODELS." Value in Health 22 (November 2019): S515. http://dx.doi.org/10.1016/j.jval.2019.09.603.

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49

Liermann, M., and R. Hilborn. "Depensation in fish stocks: a hierarchic Bayesian meta-analysis." Canadian Journal of Fisheries and Aquatic Sciences 54, no. 9 (1997): 1976–84. http://dx.doi.org/10.1139/f97-105.

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The probability of different levels of depensation within four taxonomic groups was calculated using a Bayesian technique called hierarchical modeling. With this method we combined spawner-recruit data from many stocks within a taxon to estimate the distribution describing the variability of depensation within that taxon. The spawner-recruit model we use allows for both depensation (lower than expected recruits at low population levels) and hypercompensation (where recruits are higher than expected at low population levels). The end product of our analysis is a probability distribution that ca

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50

Eddy, David M., Vic Hasselblad, and Ross Shachter. "An Introduction to a Bayesian Method for Meta-analysis." Medical Decision Making 10, no. 1 (1990): 15–23. http://dx.doi.org/10.1177/0272989x9001000104.

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