[R-meta] methods for assessing publication bias while accounting for dependency
Dear all, A lot of great suggestions here, thank you everyone. I will be sure to give a thorough read over those I'm not familiar with and follow up if any questions. I will also add another article that may be of potential interest for others by Nakagawa and colleagues (2021) Methods for testing publication bias in ecological and evolutionary meta-analyses - Nakagawa - 2022 - Methods in Ecology and Evolution - Wiley Online Library<https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13724> (p.s. my apologies for the missing post title!) Sincerely, Brendan Brendan Hutchinson Research School of Psychology ANU College of Medicine, Biology and Environment Building 39 University Ave | The Australian National University | ACTON ACT 2601 Australia T: +61 2 6125 2716 | E: brendan.hutchinson at anu.edu.au | W: Brendan Hutchinson | ANU Research School of Psychology<https://psychology.anu.edu.au/people/students/brendan-hutchinson>
From: Dr. Gerta R?cker <ruecker at imbi.uni-freiburg.de>
Sent: Tuesday, 1 March 2022 8:44 AM
To: James Pustejovsky <jepusto at gmail.com>; Lukasz Stasielowicz <lukasz.stasielowicz at uni-osnabrueck.de>
Cc: Brendan Hutchinson <Brendan.Hutchinson at anu.edu.au>; R meta <r-sig-meta-analysis at r-project.org>
Subject: Re: [R-meta] methods for assessing publication bias while accounting for dependency
Sent: Tuesday, 1 March 2022 8:44 AM
To: James Pustejovsky <jepusto at gmail.com>; Lukasz Stasielowicz <lukasz.stasielowicz at uni-osnabrueck.de>
Cc: Brendan Hutchinson <Brendan.Hutchinson at anu.edu.au>; R meta <r-sig-meta-analysis at r-project.org>
Subject: Re: [R-meta] methods for assessing publication bias while accounting for dependency
Hi all, To add the Copas selection model to the models already suggested: This model combines the usual random effects model with a selection model that models how the probability of publication depends on both a study's effect size and its standard error. In a sensitivity analysis it is investigated how effect estimates are expected to change with increasing level of selection. A goodness-of-fit test provides a plausible selection level, along with a corrected effect size, given the data. The Copas selection model is implemented in R package metasens https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fmetasens%2F&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=xMjuddRAr3m2rL%2B1%2Fxcu09DnTj0Jo44lNrlt9SB1tos%3D&reserved=0 For the implementation, see Carpenter et al. https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.jclinepi.com%2Farticle%2FS0895-4356&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=iztS2snrYcLoDOAjWB5vv53N%2FR8%2B%2BxFu%2FmAcoNqiJtE%3D&reserved=0(08)00348-X/fulltext . For a simulation study, see https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fbimj.201000151&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=2tqLGnD7s1WRoqXt4Oltd6luRfNSwqB3h8L0pg54LPQ%3D&reserved=0 . Best, Gerta Am 28.02.2022 um 21:44 schrieb James Pustejovsky: > In addition to Wolfgang's and Lukasz's suggestions, I would add that I find > the Mathur and Vanderweele approach pretty compelling. It is not exactly a > "bias adjustment" technique (as Trim and Fill or PET/PEESE purport to be) > but rather a sensitivity analysis, which examines hypothetical questions > such as: > * Supposing that statistically significant results are at most X times more > likely to be published than non-significant results, what is the maximum > degree of bias that would be expected in the overall average effect size > estimate? > * How strong would the selective publication process need to be to reduce > the overall average effect size estimate to no more than Y? > An interesting implication of their results is that there are scenarios > where an overall average effect size cannot possibly be reduced to null, > even with very extreme forms of selective publication. > > James > > On Mon, Feb 28, 2022 at 2:28 PM Lukasz Stasielowicz < > lukasz.stasielowicz at uni-osnabrueck.de> wrote: > >> Dear Brendan, >> >> unsurprisingly Wolfgang was faster than me so I'll just add one more >> reference (with further references) if your curious about the problems >> of some methods (e.g. trim and fill) even in a basic two-level >> meta-analysis: >> Carter, E. C., Sch?nbrodt, F. D., Gervais, W. M., & Hilgard, J. (2019). >> Correcting for Bias in Psychology: A Comparison of Meta-Analytic >> Methods. Advances in Methods and Practices in Psychological Science, >> 115?144. https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1177%2F2515245919847196&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=dcxYVDBLvHV6xiC%2F5QIxVkr%2FgIwuT%2BL627XkojXB5PY%3D&reserved=0 >> >> >> One other possibility to address publication bias when dealing with >> dependent effect sizes is to conduct a moderator analysis comparing >> journal articles with other sources (e.g. conference proceedings, >> dissertations). If one is willing to assume that the latter are more >> similar to unpublished literature than journal articles then the results >> of this moderator analysis approximate the mangnitude of publication >> bias. Obviously, it is only some kind of sensitivity analysis and not >> the perfect estimate of publication bias. >> >> >> Best, >> Lukasz >> -- >> Lukasz Stasielowicz >> Osnabr?ck University >> Institute for Psychology >> Research methods, psychological assessment, and evaluation >> Seminarstra?e 20 >> 49074 Osnabr?ck (Germany) >> >> Am 28.02.2022 um 19:45 schrieb r-sig-meta-analysis-request at r-project.org: >>> Send R-sig-meta-analysis mailing list submissions to >>> r-sig-meta-analysis at r-project.org >>> >>> To subscribe or unsubscribe via the World Wide Web, visit >>> https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-meta-analysis&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=5tAbqUtOHbLpAasxWYeq1fcsddkEoSC3tKJqelfgfLA%3D&reserved=0 >>> or, via email, send a message with subject or body 'help' to >>> r-sig-meta-analysis-request at r-project.org >>> >>> You can reach the person managing the list at >>> r-sig-meta-analysis-owner at r-project.org >>> >>> When replying, please edit your Subject line so it is more specific >>> than "Re: Contents of R-sig-meta-analysis digest..." >>> >>> >>> Today's Topics: >>> >>> 1. Re: methods for assessing publication bias while accounting >>> for dependency (Viechtbauer, Wolfgang (SP)) >>> 2. Re: Heterogeneity and moderated mediation (Michael Dewey) >>> 3. Re: Meta-analysis of prevalence data: back-transformation >>> and polytomous data (Viechtbauer, Wolfgang (SP)) >>> 4. Re: Importing Correlations from PDF to table format (Kiet Huynh) >>> >>> ---------------------------------------------------------------------- >>> >>> Message: 1 >>> Date: Mon, 28 Feb 2022 13:31:46 +0000 >>> From: "Viechtbauer, Wolfgang (SP)" >>> <wolfgang.viechtbauer at maastrichtuniversity.nl> >>> To: Brendan Hutchinson <Brendan.Hutchinson at anu.edu.au>, >>> "r-sig-meta-analysis at r-project.org" >>> <r-sig-meta-analysis at r-project.org> >>> Subject: Re: [R-meta] methods for assessing publication bias while >>> accounting for dependency >>> Message-ID: <2377cc39202643a0ac5d87a34fce3cda at UM-MAIL3214.unimaas.nl> >>> Content-Type: text/plain; charset="iso-8859-1" >>> >>> Dear Brendan, >>> >>> Using the 'regression method' approach could also be regarded as a form >> of sensitivity analysis, when focusing on the model intercept as an >> estimate of the 'adjusted' effect (as in the PET/PEESE methods). In fact, >> if I recall the findings from various simulation studies, this seems to >> work better than the trim and fill method. >>> One can also aggregate the estimates to the study level (or to whatever >> level needed so that the resulting aggregated values can be assumed to be >> independent) and then run methods that assume independence on these >> aggregated data (including trim and fill). >>> Another recent method by James Pustejovsky: >> https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.jepusto.com%2Ftalk%2Fstanford-qsu-2022-selective-reporting%2F&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=o%2F9eDngAzezyVcmON2IxZv5lBXEE0Q952cw%2F95quPAQ%3D&reserved=0 >>> Some other relevant readings: >>> >>> Fern?ndez-Castilla, B., Declercq, L., Jamshidi, L., Beretvas, S. N., >> Onghena, P. & Van den Noortgate, W. (2021). Detecting selection bias in >> meta-analyses with multiple outcomes: A simulation study. The Journal of >> Experimental Education, 89(1), 125-144. >> https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1080%2F00220973.2019.1582470&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=csohpod%2FoNwkKjmUoCQGyCqcpWJsGQR19aFkVYIBKRs%3D&reserved=0 >>> Rodgers, M. A. & Pustejovsky, J. E. (2021). Evaluating meta-analytic >> methods to detect selective reporting in the presence of dependent effect >> sizes. Psychological Methods, 26(2), 141-160. >> https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1037%2Fmet0000300&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=ZjP0peHV2hSO7BtXPEanEo%2BcAlxgnPsEUUaZEHRd05o%3D&reserved=0 >>> P.S.: Please use meaningful post titles to make the mailing list >> archives more useful. >>> Best, >>> Wolfgang >>> >>>> -----Original Message----- >>>> From: R-sig-meta-analysis [mailto: >> r-sig-meta-analysis-bounces at r-project.org] On >>>> Behalf Of Brendan Hutchinson >>>> Sent: Friday, 25 February, 2022 14:15 >>>> To: r-sig-meta-analysis at r-project.org >>>> Subject: [R-meta] (no subject) >>>> >>>> Dear mailing list, >>>> >>>> I have a couple of minor questions regarding methods for assessing >> publication >>>> bias while accounting for dependency. >>>> >>>> To my understanding, there is no means of running a publication bias >> analysis, >>>> such as trim and fill, with a multilevel meta-analytic model in R (or a >> model in >>>> which dependency issues need be accounted for). I am aware that one can >> use a >>>> regression method, such as regressing the standard error onto the >> summary >>>> estimate, within a multi-level model (this is fairly straightforward >> using >>>> rma.mv(), for example). However, what about methods for assessing the >> robustness >>>> of findings, if publication bias is a concern (such as trim and fill), >> while also >>>> accounting for dependency? >>>> >>>> The best I have found is a recent package "PublicationBias" by Mathur >> and >>>> VanderWeele (10.1111/rssc.12440). >>>> >>>> I am wondering if anyone has any recommendations for particular >> methods, R >>>> packages, or readings? >>>> >>>> Thanks so much! >>>> >>>> Brendan >>> >>> >>> >>> ------------------------------ >>> >>> Message: 2 >>> Date: Mon, 28 Feb 2022 14:05:08 +0000 >>> From: Michael Dewey <lists at dewey.myzen.co.uk> >>> To: Amy Zadow <Amy.Zadow at unisa.edu.au>, R meta >>> <r-sig-meta-analysis at r-project.org> >>> Subject: Re: [R-meta] Heterogeneity and moderated mediation >>> Message-ID: <e560f46d-9887-d498-8c01-fa63b87fae24 at dewey.myzen.co.uk> >>> Content-Type: text/plain; charset="windows-1252"; Format="flowed" >>> >>> It is hard to comment in detail as we do not have any information about >>> the meta-analysis you ran. Are there two separate analyses, one for >>> groups and one for individuals, two separate data-sets, one for groups >>> and one for individuals, or one analysis using a multi-level >>> meta-analysis? Presumably that is all replicated four times for each PSC >>> (whatever that is) but that could equally be another level in the >>> multi-level mode. >>> >>> Would the nature of the research environment and study design have >>> caused you to believe that heterogeneity was expected or unlikely? >>> >>> Michael >>> >>> On 28/02/2022 06:50, Amy Zadow wrote: >>>> Hello, >>>> >>>> I am seeking advice about my current results ? any >>>> comments/criticism/advice around the heterogeneity statistics would be >>>> very helpful >>>> >>>> Also I would be keen to conduct a moderated mediation but not sure where >>>> to start. Any advice/ recommended resources or code would be much >>>> appreciated. >>>> >>>> Many thanks, Amy >>>> >>>> >>>> > _______________________________________________ >>>> > R-sig-meta-analysis mailing list >>>> > R-sig-meta-analysis at r-project.org >>>> <mailto:R-sig-meta-analysis at r-project.org> >>>> > https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-meta-analysis&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=5tAbqUtOHbLpAasxWYeq1fcsddkEoSC3tKJqelfgfLA%3D&reserved=0 >>>> 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[[alternative HTML version deleted]] > > _______________________________________________ > R-sig-meta-analysis mailing list > R-sig-meta-analysis at r-project.org > https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-meta-analysis&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=5tAbqUtOHbLpAasxWYeq1fcsddkEoSC3tKJqelfgfLA%3D&reserved=0 -- Dr. rer. nat. Gerta R?cker, Dipl.-Math. Guest Scientist Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg Zinkmattenstr. 6a, D-79108 Freiburg, Germany Mail: ruecker at imbi.uni-freiburg.de Homepage: https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.uniklinik-freiburg.de%2Fimbi-en%2Femployees.html%3Fimbiuser%3Druecker&data=04%7C01%7CBrendan.Hutchinson%40anu.edu.au%7C9efe42f73c3649c5a8e708d9fb038477%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637816816051249581%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0&sdata=dJ2qxjW7KjwJ4A1289t1nL2yEnmWHgXWvYyRk5%2Bnefw%3D&reserved=0