Business Ethics Journal Article
sandeep1234
Relationshipsamongcommercialpracticesandauthorconflictsofinterestinbiomedicalpublishing.pdf
RESEARCH ARTICLE
Relationships among commercial practices
and author conflicts of interest in biomedical
publishing
S. Scott GrahamID 1*, Zoltan P. Majdik2☯, Dave Clark3☯, Molly M. Kessler4‡, Tristin
Brynn Hooker 1‡
1 Department of Rhetoric & Writing, University of Texas at Austin, Austin, Texas, United States of America,
2 Department of Communication, North Dakota State University, Fargo, North Dakota, United States of
America, 3 Department of English, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States
of America, 4 Department of Writing Studies, University of Minnesota-Twin Cities, Minneapolis, Minnesota,
United States of America
☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work.
Abstract
Recently, concerns have been raised over the potential impacts of commercial relationships
on editorial practices in biomedical publishing. Specifically, it has been suggested that cer-
tain commercial relationships may make editors more open to publishing articles with author
conflicts of interest (aCOI). Using a data set of 128,781 articles published in 159 journals,
we evaluated the relationships among commercial publishing practices and reported author
conflicts of interest. The 159 journals were grouped according to commercial biases (reprint
services, advertising revenue, and ownership by a large commercial publishing firm). 30.6%
(39,440) of articles were published in journals showing no evidence of evaluated commercial
publishing relationships. 33.9% (43,630) were published in journals accepting advertising
and reprint fees; 31.7% (40,887) in journals owned by large publishing firms; 1.2% (1,589) in
journals accepting reprint fees only; and 2.5% (3,235) in journals accepting only advertising
fees. Journals with commercial relationships were more likely to publish articles with aCOI
(9.2% (92/1000) vs. 6.4% (64/1000), p = 0.024). In the multivariate analysis, only a journal’s
acceptance of reprint fees served as a significant predictor (OR = 2.81 at 95% CI, 1.5 to
8.6). Shared control estimation was used to evaluate the relationships between commercial
publishing practices and aCOI frequency in total and by type. BCa-corrected mean differ-
ence effect sizes ranged from -1.0 to 6.1, and confirm findings indicating that accepting
reprint fees may constitute the most significant commercial bias. The findings indicate that
concerns over the influence of industry advertising in medical journals may be overstated,
and that accepting fees for reprints may constitute the largest risk of bias for editorial deci-
sion-making.
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 1 / 11
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Graham SS, Majdik ZP, Clark D, Kessler
MM, Hooker TB (2020) Relationships among
commercial practices and author conflicts of
interest in biomedical publishing. PLoS ONE 15(7):
e0236166. https://doi.org/10.1371/journal.
pone.0236166
Editor: Omid Beiki, Karolinska Institutet, SWEDEN
Received: February 12, 2020
Accepted: June 30, 2020
Published: July 24, 2020
Copyright: © 2020 Graham et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data are available
in a public repository at the following location: 3.
Graham, S.S., Majdik, Z., & Clark, D. (2020).
Conflicts of Interest: Article XML. [Data file]. Texas
Data Repository. DOI: 10.18738/T8/VSWAJY.
Funding: Funding is provided by National
Endowment for the Humanities grant HAA-261070.
The funder had no role in the analysis or
interpretation of data, the writing of this report, or
the decision to submit the article for publication.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
For some time now, there has been growing concern about the extent to which financial rela-
tionships with industry bias the results of biomedical research. Studies of industry funding and
author conflicts of interest (aCOI) in the biomedical sciences have found that these financial
relationships can bias choices in experimental design [1–3] as well as clinical decision-making
during trial execution [4–6]. In particular, the most recent studies and meta-analyses confirm
that these financial relationships and associated practices result in a substantial increase in the
likelihood that clinical trial results will be favorable to industry [7–9]. Recent research in these
areas also points toward an ever-widening array of potentially biasing practices, including
ghost authorship [10–11] and so-called "marketing trials”—i.e., clinical trials that were
designed primarily to influence medical decision-making in favor of product use [12–13] .
Despite movements toward greater transparency in disclosing aCOI in medical journals,
including the International Committee for Medical Journals Editors (ICJME) recommenda-
tions for reporting aCOI [14], inconsistencies still remain in reporting financial and non-
financial COI for authors, researchers, and editors. This issue is particularly acute regarding
the relative inconsistency and opacity of editorial COI disclosures, a concern that often persists
even when author and researcher disclosures become more transparent [15–18].
In addition to worries over personal COI that may be held by journal editors, there are also
growing apprehensions over the potential effects of certain commercial publishing practices
on biomedical research. Specifically, it has been suggested that journal-level financial relation-
ships such as the acceptance of industry advertising revenue, reprint fees, and additional
industry printing contracts held by journal parent companies may impact editorial decision-
making, creating an environment more favorable to industry-sponsored research [19–21]. Edi-
tor COI and potential commercial publishing biases may be of particular concern given recent
fears that non-peer-reviewed publications with aCOI are having significant impacts on bio-
medical research and clinical practice [22]. Certainly, available anecdotal evidence does sug-
gest there may be cause for concern [23,24]. Two of the most notable cases involve the
punitive withdrawal of $1.5 million in advertising revenue from the Annals of Internal Medi- cine following the publication of an article critiquing multiple industry-funded trials in 1992 [20] and Merck’s dispersal of $836,000 to the New England Journal of Medicine for reprints of the VIGOR study as a part of the Vioxx marketing campaign [25]. Furthermore, there are
some data available indicating that commercial publishing biases may lead to editors being less
diligent in the execution of journal aCOI policies for article authors [19–20]. As greater atten-
tion is paid to the potential adverse consequences of aCOI and industry funding on medical
research, it is critical that ongoing discussions regarding potential commercial publishing
biases occur in an evidence-rich environment.
Accordingly, this study evaluates the relationships between potential commercial publish-
ing biases and industry favorability (as measured by aCOI likelihood and frequency) in 159
biomedical journals. Research in a variety of subspecialties has demonstrated that aCOI fre-
quently associates with results favorable to industry [26–31], and more recently an analysis
across clinical subspecialties indicates aCOI predicts that research will be 2.94 times more
likely to return favorable results [32] Subsequently aCOI can serve as an effective surrogate
endpoint for measuring industry favorability more broadly. In what follows, we describe our
development of a machine-learning framework for identifying and classifying aCOI. We then
compare aCOI likelihood and frequency in journal samples stratified by identified commercial
relationships. The results show that the presence of some commercial relationships does
appear to create an environment more favorable to scholarship with aCOI, increasing the like-
lihood that published articles will have aCOI as well as the number of aCOI per article. In
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 2 / 11
particular, the acceptance of reprint orders appears to be the most influential of evaluated
potential commercial biases.
Methods
In order to enhance the available evidence base regarding the potential influence of commer-
cial publishing practices on editorial decision-making, we collected 128,781 biomedical journal
articles indexed with Medline. In 2016 Medline began collecting conflicts of interest informa-
tion from participating journals. We extracted data for analysis in January 2019, and at that
time, approximately 30 million articles were indexed in the database. The population of
128,781 articles was identified first by extracting all MEDLINE-indexed articles (2016–2018)
with aCOI disclosure statements (N = 274,246). These articles were published in a total of 1497
journals. Our final sample of 128,781 articles in 159 journals was derived by excluding all arti-
cles where the publishing journal was present fewer than 25 times in the full dataset. We evalu-
ated the presence and rate of aCOI across all articles in the dataset using a custom-built
automated parser and compared aCOI likelihood and quantity to suggested measures of com-
mercial biases. In what follows, we describe our approaches to 1) aCOI identification and clas-
sification, 2) evaluating the reliability of the aCOI parser, and 3) identifying the presence or
absence of commercial publishing practices in each journal.
Author COI identification & classification
In order to identify and classify each of the reported aCOIs in these disclosure statements, we
developed a metadata assisted, machine-learning enhanced, natural language processing
(NLP) tool. In short, the parser uses a trained language model to tag sponsors (e.g., pharma-
ceutical companies). The parser then uses Medline author metadata to identify named authors
in the disclosure statements, matches authors to sponsors, and finally identifies the type of
conflict disclosures. Each of these parser stages are described in more detail below.
Sponsor identification. An NLP method called Named Entity Recognition (NER) uses
grammatical and/or statistical techniques to extract and classify entities like persons, locations,
dates, or organizations from unstructured text. For example, a sentence such as “Walter San-
dulli and Jessica Goldenberg are employees of Akrimax,” when parsed, would produce three
“named entities”: Walter Sandulli, PERSON; Jessica Goldenberg, PERSON; and Akrimax,
ORG. NER approaches can work accurately on unknown texts, and can achieve high levels of
precision when trained using a machine learning approach. But in the case of disclosure state-
ments, the lack of consistent styling in the writing and editing of COI statements means that
organization names are presented very differently, sometimes within the same COI statement
(e.g., GlaxoSmithKline vs. Glaxo vs. GSK). COI statements are similarly inconsistent in pre-
senting author names; often they use initials, but sometimes last names or other abbreviations
will be present. These inconsistencies, coupled with the fact that pharmaceutical company
names often resemble proper names, can challenge an out-of-the-box NER model. Using a
basic English language model trained on a small sample of human-corrected COI statements
(n = 100), we were able to decrease the sponsor identification error rate by 68% compared to
the default model.
Author identification. Our approach used MEDLINE data on author names to further
increase recognition accuracy for both author names and organizations. In light of the author
naming conventions described above, as well as the fact that organizations in the biomedical
field often have names that, to a computer, resemble human names (e.g., the “Smith Kline” of
GlaxoSmithKline), automated NER parsing will frequently mischaracterize organizations as
names, and vice versa. To counteract this issue, the parser uses author metadata to generate an
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 3 / 11
author-name permutation table with 13 name permutations that correspond to author naming
conventions from various journal style guides for disclosure statements. “Jane Alicia Doe,” for
example, would be rendered as “J.A.D.,” “J. Doe,” “J Doe,” and ten other permutations of first,
middle, and last name and/or initials. Using this metadata-generated list of author permuta-
tions instead of relying on the NER to tag both authors and organizations allowed us to not
only have a high degree of precision in identifying authors in COI statements, but also to
cross-check them against entities tagged as organizations and remove them if they were in the
author list.
aCOI classification. The aCOI classification dictionary is based loosely on the Interna-
tional Committee of Medical Journal Editors (ICMJE) standardized conflicts of interest disclo-
sure form. Our COI dictionary schema organizes these categories (as well as employment in
industry) into a three-level schema based on potential benefit from a product’s success. Specifi-
cally, low-level aCOI included personal fees, travel, board memberships, and non-financial
support. Mid-level aCOI included grants and research support. Finally, high-level aCOI
included stock ownership and employment in industry. The parser assumes a standard syntax
that almost all COI disclosure statements follow, where a name (or names) are followed by an
aCOI disclosure type (like “is employed by”), which is followed by the aCOI source. The parser
extracts aCOI value(s) from each disclosure statement by stitching the three elements
described above—NER, author permutations, aCOI classifications—together through a regular
expression. This process is repeated for each tagged sponsor in a disclosure statement. Outputs
are collated and assigned a numerical weight based on the aCOI classification dictionary.
Table 1 provides an example of a fully parsed disclosure statement.
Parser reliability. In order to evaluate the reliability of the aCOI parser, a random sample
of 1000 disclosure statements was submitted to human evaluation. While the dataset includes
128,781 disclosure statements, the results of our analysis indicate that approximately 94% of
these are some version of "The authors report no conflicts of interest." A truly representative
sample of 1000 disclosure statements would thus only provide 60 statements for the human or
parser to evaluate. Therefore, our sampling protocol excluded disclosure statements of fewer
than 50 characters (i.e., those more likely to be some variation of “The authors report no con-
flicts of interest,” which is 44 characters long. The end result of this approach is that we over-
sampled disclosure statements where aCOIs were more likely to be present.
In order to compare the human-coded and machine-coded samples, we assessed reliability
using the two-way average measure Intra-Class Correlation Coefficient (ICC). The average
ICC for low-level conflicts was 0.722, with a 95% confidence interval from 0.69 to 0.751 (F
[998,903 [= 6.27, p < .01). The average ICC for medium weight conflicts was 0.773, with a 95%
confidence level from 0.747 to 0.797 (F[998,985] = 7.84, p < .01). And, finally, the average ICC
for high-level conflicts was 0.618, with a 95% confidence level from 0.578 to 0.656 (F[998,923]
= 4.28, p < .001).
Table 1. Fully-parsed disclosure statement for “defining priorities for future research: Results of the UK kidney transplant priority setting partnership” (PMID:
27776143).
Author Relationship Type Sponsor Conflict Weight
Simon Ball grant Oxford 2
Simon Knight fees OrganOx UK 1
Lorna Marson fees Novartis 1
Lorna Marson fees Astellas 1
Fiona Loud fees Merck 1
Graham Lipkin fees Raptor Pharmaceuticals 1
Graham Lipkin fees Alexion Pharma 1
https://doi.org/10.1371/journal.pone.0236166.t001
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 4 / 11
Recommendations for appropriate ICC thresholds vary somewhat across disciplines and
contexts. The threshold of “low” agreement can be from below ICC = 0.40 [33] to ICC = 0.50
[34]. Fair to moderate agreement thresholds vary the most with recommend ranges from ICC =
0.40 to ICC = 0.75 [35]. Most ICC schemata accept ICC > 0.60 as fair to good and ICC > 0.75 as
good to excellent. Since identifying the absence of conflicts is an easier computational task than
conflict classification, our approach here invariably resulted in lower ICC scores than would be
expected in a truly representative sample. However, the benefit of this approach is that it ensured
the parser evaluation would involve a much wider variety of conflict types. Nevertheless, parser
reliability scores generally fell within ranges that would be classified as moderate to good.
Commercial relationships identification
Potential sources of commercial bias were identified based on the extant literature. Research
and opinion pieces published in biomedical journals regularly identify the acceptance of adver-
tising revenue, the acceptance of reprint contracts, and the parent company’s acceptance of
industry publishing contracts (e.g. supplements) as potential sources of editorial bias [20,36].
Therefore, we reviewed journal websites for solicitations of adverting revenue and reprint fees.
Additionally, for each journal, the parent company of the journal was identified. This informa-
tion is typically available in a website header or footer and/or on the “About” page. If the par-
ent company was primarily a publishing firm (e.g. Elsevier, Taylor and Francis, Wiley), the
journal was assigned to the large publishing firm category. Every journal in the dataset that
was owned by a large publishing firm accepted adverting and reprint fees. Thus we were able
to assign each journal to one of the following categories: 1) control group (accepts no advertis-
ing, reprint fees, not owned by commercial publishing group); 2) accepts advertising revenue,
but not reprint fees, 3) accepts reprint fees, but not adverting revenue, 3) accepts both advertis-
ing and reprint fees, but is not owned by a large commercial publisher, and 4) owned by a
commercial publishing firm.
Results
We evaluated aCOI rates for 128,781 articles published in 159 journals indexed by MEDLINE.
Each journal in the dataset included at least 25 articles, with PLoS One having the most at 22,252 articles. By group, the dataset included 43,630 articles in journals accepting advertising
and reprint fees, but not belonging to a commercial publishing firm; 40,887 articles in journals
owned by large publishing firms; 1,589 in journals accepting reprint fees but not advertising
fees; 3,235 in journals accepting only advertising fees; and 39,440 articles in the control group.
Table 2 details these numbers alongside aCOI rates.
aCOI frequency analysis
An initial test for equality of proportions (using Yates’ continuity correction) was conducted
to assess if articles with aCOI were more likely to be published in journals with potential
Table 2. Number of articles and aCOI rates by journal commercial relationships category.
Group n Low aCOI Mid aCOI High aCOI Total aCOI
Control 39440 6912 4960 6451 18326
Reprints 1589 982 750 242 1974
Ad Revenue 3235 600 266 1975 1147
AdRev + Reprints 43630 34425 24741 6180 65346
CommPub 40887 20834 12924 6806 40564
https://doi.org/10.1371/journal.pone.0236166.t002
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 5 / 11
commercial biases. In order to ensure adequate statistical power (β = .9) for the test, a random sample of 1,000 articles was selected from each set of journals (with vs. without commercial
biases). Journals with potential commercial biases published articles with aCOI at a rate of
9.2% whereas those without potential commercial biases published articles with aCOI at a rate
of 6.4%. This difference is significant [χ2 (1, N = 2000) = 5.07, p = 0.024]. In order to evaluate whether some commercial relationships were more predictive of these
frequency differences, we identified the rate at which articles with aCOI were published in
each journal in the data set. These data were fitted to a quasi-binomial multiple regression
model with a logit link. Overall, the model was significant at F(23.33, 2496) = 3.51, p = 0.017.
Neither advertising revenue nor ownership by a large commercial publishing firm were signifi-
cant predictors of aCOI likelihood. However, a journal’s acceptance of reprints predicts that
the likelihood that a published article will have aCOI increases by a factor of 2.81(95% CI, 1.5
to 8.6, p = 0.0416).
aCOI rates analysis
Shared-control estimation plots were used to compare aCOI rates across journal categories.
Shared-control estimation plots are part of the estimation statistics framework recently pro-
mulgated as a robust alternative to null-hypothesis significance testing [37]. Estimation plots
focus analytic attention on population parameters, mean differences, and effect sizes over p-
values. The approach here uses Efron’s technique for bias-corrected accelerated bootstrap
(BCa) estimation to account for skewed populations [38]. Using a stratified sample of articles
in each group, we ran 5,000 BCa iterations at the 95% confidence level in order to derive the
effect estimates reported in Table 3.
The results of the estimation plots (Fig 1) indicate that certain commercial practices have a
modest effect on the number of aCOI per article. Of course, these effects are not equal across
categories or measures. In terms of total aCOI, the effect size is so modest as to be negligible
for journals accepting either reprints or ad revenue. However, for journals accepting both
reprints and ad revenue the effect size would functionally double the aCOI rate for the average
article, whereas ownership by a large commercial publishing firm would increase the aCOI
rate of the average article by approximately 60%. However, when aCOI are separated by
weight, a more complex picture emerges. Among the aCOI-level specific plots, the largest
effect sizes appear on the plot for low-level aCOIs. Interestingly, all the effects are negative on
the plot for high-level aCOIs. However, the findings for high-level aCOI should be interpreted
cautiously given the more moderate inter-rater agreement rates. [39]
Nevertheless, certain patterns emerge when looking across tests. The data suggest there
may be an aggregation effect whereby an increased number of commercial relationships may
result in a greater willingness to publish articles with higher aCOI rates. In each of the tests for
total aCOI, low-level aCOI, and mid-level aCOI, journals that accept both ad revenue and
reprints account for the greatest effects. A striking finding from this study is that in three of
the four tests, the effect of advertising revenue, in isolation, is negative. In the remaining case,
the effect size is negligible.
Table 3. Effect size expressed as BCa-corrected (5000 iterations) mean difference compared to the control.
Group Low aCOI Mid aCOI High aCOI Total aCOI
Reprints 1.55 1.40 -1.44 1.52
Ad Revenue 0.39 -0.47 -1.05 -1.13
AdRev + Reprints 4.07 3.27 -1.16 6.18
CommPub 2.75 0.97 -0.52 3.20
https://doi.org/10.1371/journal.pone.0236166.t003
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 6 / 11
Discussion
Editorial decision-making is a complex matter driven by a multitude of competing factors,
many of which are not accounted for in the literature on the potential impacts of commercial
Fig 1. Shared-control estimation plots for aCOI by level and total aCOI.
https://doi.org/10.1371/journal.pone.0236166.g001
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 7 / 11
publishing relationships. As biomedical publishing works to address these impacts within the
context of broader concerns over COI and industry funding, it is imperative that new and
revised policies are reflective of the best evidence available. The potential dangers of adopting
new COI policies in the context of a dearth of evidence has become clear following the discov-
ery of unanticipated and pernicious effects of COI disclosure [40,41]. Disclosure statements
have been shown to cause audiences to extend more trust to those holding conflicts of interest
as disclosure provides an opportunity to display both honesty and expertise. Conflict disclo-
sure can also lead to "moral licensing," a phenomenon whereby those who disclose conflicts
become unduly confident in their objectivity because transparency obligations have been ful-
filled. In order to mitigate the risks of such unanticipated consequences in future policy pro-
posals, recommendations must be based on a solid evidentiary foundation.
The results presented in this article work toward that end with respect to addressing poten-
tial commercial biases in biomedical publishing. One additional benefit of this study, com-
pared to many others in the area, is that it includes non-clinical trial publications. As
mentioned above, perspectives, comments, opinion pieces, and recruited articles are often
selected solely on the basis of editorial discretion. As such, they may be especially open to com-
mercial publishing biases. Additionally, the available data suggests that readers of biomedical
journals are not always sensitive to the differences in peer- or editorially revised content. As
such, these non-peer-reviewed publications may be exercising undue influence on practitioner
understandings on the state of medical science.
Ultimately, the results presented here indicate that the presence of commercial publishing
relationships predicts increases in industry favorability as measured by aCOI frequency and
quantity. In particular, the data indicate that accepting reprint fees increases the likelihood
that any given article published in a journal will have reported aCOI by a factor of 2.81. Addi-
tionally, these data show modest effects on the average quantity of aCOI in conflicted articles.
That is, when journals accept both advertising revenue and reprint fees or belong to large com-
mercial publishing firms, we see a modest increase in the average total aCOI per article. Inter-
estingly, however, the results of the aCOI quantity analysis indicate that accepting advertising
revenue, in isolation, has a modest negative effect on average aCOI per article. Finally, the data
indicate that commercial publishing biases have a negligible, but negative, effect on average
number of high-level aCOI per article.
Even though advertising revenue has been subject to the greatest scrutiny in the literature,
it may represent the lowest cause for concern among the commercial biases evaluated in this
study. This may indicate that something like the journalistic invisible wall is functioning
appropriately in biomedical journals. Ultimately, these data indicate that the acceptance of fees
for reprints may be the most impactful on commercial bias. In some respects, this makes
sense. The potential for reprint revenue is the bias most directly tied to editorial decision-mak-
ing. That is, the choice to publish a study favorable to industry, especially when that study
might suggest new or expanded use of a drug, can be directly traced to reprint revenue.
Despite the suggestive nature of these findings, additional research should be conducted to
verify and extend results. Specifically, future studies might further validate aCOI as an effective
surrogate endpoint for industry favorability. While a number of studies do indicate that it can
be used as such, research design and findings are not entirely uniform [42–44]. However, it
has been suggested that some studies that do not indicate that aCOI predicts results favorable
to industry may be underpowered [32]. Consequently, one important limitation of this study
comes from low participation in Medline’s aCOI reporting program among many of the
world’s top medical journals. Indeed, among the ten highest h-index medical journals, only
one (BMJ) reports aCOI to Medline. Despite the above-mentioned limitations of disclosure statements, the availability of aCOI data has a real impact on our ability to evaluate potential
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 8 / 11
risks. It would be helpful if more high-profile journals participated in Medline’s program. In
the absence of such participation, supplementary research that collects data directly from tar-
get publications may be in order.
Additionally, further research should be conducted with respect to the impacts of commer-
cial publishing relationships on other markers of editorial decision making. For example,
researchers might take inspiration from the recent study identifying the prevalence of market-
ing trials across journals [13]. Replicating this study with a data set stratified across journals
representing a range of commercial biases would further add to the evidentiary foundation
necessary to develop sound policies on commercial biases. New research might also use data
on the prevalence of ghost authorship or improperly reported aCOI across journals to evaluate
associations with commercial biases. In the meantime, the results presented here suggest that,
as these data are being curated, attention should probably be focused on commercial publish-
ing biases that can be tied most directly to editorial decision-making, specifically the collection
of reprint revenues.
Author Contributions
Conceptualization: S. Scott Graham.
Data curation: S. Scott Graham, Dave Clark, Molly M. Kessler, Tristin Brynn Hooker.
Formal analysis: S. Scott Graham, Zoltan P. Majdik, Dave Clark, Molly M. Kessler.
Funding acquisition: S. Scott Graham.
Investigation: S. Scott Graham, Zoltan P. Majdik, Dave Clark, Molly M. Kessler, Tristin
Brynn Hooker.
Methodology: S. Scott Graham, Zoltan P. Majdik, Dave Clark.
Project administration: S. Scott Graham.
Resources: S. Scott Graham, Zoltan P. Majdik.
Software: S. Scott Graham, Zoltan P. Majdik, Dave Clark.
Supervision: S. Scott Graham.
Validation: S. Scott Graham, Zoltan P. Majdik, Dave Clark, Molly M. Kessler.
Visualization: S. Scott Graham.
Writing – original draft: S. Scott Graham, Zoltan P. Majdik, Molly M. Kessler, Tristin Brynn
Hooker.
Writing – review & editing: S. Scott Graham, Zoltan P. Majdik, Dave Clark, Molly M. Kessler,
Tristin Brynn Hooker.
References
1. Lexchin J. Those who have the gold make the evidence: how the pharmaceutical industry biases the
outcomes of clinical trials of medications. Sci Eng Ethics. 2012; 18(2):247–261. https://doi.org/10.1007/
s11948-011-9265-3 PMID: 21327723
2. Lexchin J. 2012b. Sponsorship bias in clinical research. Int J Risk Saf Med. 2012; 24(4):233–242.
https://doi.org/10.3233/JRS-2012-0574 PMID: 23135338
3. Sismondo S. How pharmaceutical industry funding affects trial outcomes: causal structures and
responses. Soc Sci Med. 2008; 66(9):1909–1914. https://doi.org/10.1016/j.socscimed.2008.01.010
PMID: 18299169
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 9 / 11
4. Lundh A, Krogsbøll LT, Gøtzsche PC. Sponsors’ participation in conduct and reporting of industry trials: a descriptive study. Trials. 2012; 13:146. https://doi.org/10.1186/1745-6215-13-146 PMID: 22920226
5. Rochon PA, Sekeres M, Hoey J, Lexchin J, Ferris LE, Moher D, et al. Investigator experiences with
financial conflicts of interest in clinical trials. Trials. 2011 Dec; 12(1):9.
6. Sismondo S. Ghost management: how much of the medical literature is shaped behind the scenes by
the pharmaceutical industry? PLoS Med. 2007; 4(9):e286. https://doi.org/10.1371/journal.pmed.
0040286 PMID: 17896859
7. Ahn R, Woodbridge A, Abraham A, Saba S, Korenstein D, Madden E, et al. Financial ties of principal
investigators and randomized controlled trial outcomes: cross sectional study. BMJ, 2017; 356:i6770.
https://doi.org/10.1136/bmj.i6770 PMID: 28096109
8. Lundh A, Lexchin J, Mintzes B, Schroll JB, Bero L. Industry sponsorship and research outcome.
Cochrane Database Syst Rev. 2017; 2: MR000033. https://doi.org/10.1002/14651858.MR000033.pub3
PMID: 28207928
9. Flacco ME, Manzoli L, Boccia S, Capasso L, Aleksovska K, Rosso A, et al. 2015. Head-to-head ran-
domized trials are mostly industry sponsored and almost always favor the industry sponsor. J Clin Epi-
demiol. 2015; 68(7):811–820. https://doi.org/10.1016/j.jclinepi.2014.12.016 PMID: 25748073
10. Matheson A. Ghostwriting: the importance of definition and its place in contemporary drug marketing.
BMJ, 2016; 354:i4578. https://doi.org/10.1136/bmj.i4578 PMID: 27576493
11. DeTora LM, Carey MA, Toroser D, Baum EZ. Ghostwriting in biomedicine: a review of the published lit-
erature. Current medical research and opinion. 2019 Sep 2; 35(9):1643–51. https://doi.org/10.1080/
03007995.2019.1608101 PMID: 30986084
12. Matheson A. Marketing trials, marketing tricks—how to spot them and how to stop them. Trials. 2017;
18(1):105. https://doi.org/10.1186/s13063-017-1827-5 PMID: 28270221
13. Barbour V, Burch D, Godlee F, Heneghan C, Lehman R, Perera R, et al. Characterisation of trials
where marketing purposes have been influential in study design: a descriptive study. Trials. 2016; 17
(1):31.
14. International Committee of Medical Journal Editors (ICJME). Conflicts of interest. 2019. http://www.
icmje.org/conflicts-of-interest/.
15. Shawwa K, Kallas R, Koujanian S, Agarwal A, Neumann I, Alexander P, et al. Requirements of clinical
journals for authors’ disclosure of financial and non-financial conflicts of interest: a cross sectional
study. PLoS One. 2016; 11(3):e0152301. https://doi.org/10.1371/journal.pone.0152301 PMID:
27030966
16. Liu JJ, Bell CM, Matelski JJ, Detsky AS, Cram P. Payments by US pharmaceutical and medical device
manufacturers to US medical journal editors: retrospective observational study. BMJ. 2017; 359:j4619.
https://doi.org/10.1136/bmj.j4619 PMID: 29074628
17. Mehlman CT, Okike K, Bhandari M, Kocher MS. Potential financial conflict of interest among physician
editorial board members of orthopaedic surgery journals. JBJS. 2017; 99(5)Lp.e19.
18. Janssen SJ, Bredenoord AL, Dhert W, de Kleuver M, Oner FC, Verlaan JJ. Potential conflicts of interest
of editorial board members from five leading spine journals. PloS One. 2015; 10(6):e0127362. https://
doi.org/10.1371/journal.pone.0127362 PMID: 26042410
19. Easley TJ, Medical journals, publishers, and conflict of interest. JAMA. 2017; 317(17):1759–1760.
https://doi.org/10.1001/jama.2017.3421 PMID: 28464164
20. Lexchin J, Light DW. Commercial influence and the content of medical journals. BMJ. 2006; 332
(7555):1444–1447. https://doi.org/10.1136/bmj.332.7555.1444 PMID: 16777891
21. Handel AE, Patel SV, Pakpoor J, Ebers GC, Goldacre B, Ramagopalan SV. 2012. High reprint orders in
medical journals and pharmaceutical industry funding: case-control study. BMJ. 2012: 344:e4212.
https://doi.org/10.1136/bmj.e4212 PMID: 22745328
22. Fontanarosa P, Bauchner H. Conflict of interest and medical journals. JAMA. 2017; 317(17):1768–
1771. https://doi.org/10.1001/jama.2017.4563 PMID: 28464125
23. Dyer O. Journal rejects article after objections from marketing department. BMJ. 2004; 328:244
24. Tsai AC. Conflicts between commercial and scientific interests in pharmaceutical advertising for medi-
cal journals. Int J Health Serv. 2003; 33(4):751–768. https://doi.org/10.2190/K0JG-EXG1-FB12-0ANF
PMID: 14758858
25. Smith R. Lapses at the New England Journal of Medicine. J Res Soc Med. 2006; 99(8):380–382.
26. Amiri AR, Kanesalingam K, Cro S, Casey AT. Does source of funding and conflict of interest influence
the outcome and quality of spinal research? Spine 2014; 356:308–14.
27. Perlis RH, Perlis CS, Wu Y, Hwang C, Joseph M, Nierenberg AA. Industry sponsorship and financial
conflict of interest in the reporting of clinical trials in psychiatry. Am J Psychiatry 2005; 356:1957–60.
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 10 / 11
28. Perlis CS, Harwood M, Perlis RH. Extent and impact of industry sponsorship conflicts of interest in der-
matology research. J Am Acad Dermatol 2005; 356:967–71.
29. Riechelmann RP, Wang L, O’Carroll A, Krzyzanowska MK. Disclosure of conflicts of interest by authors
of clinical trials and editorials in oncology. J Clin Oncol 2007; 356:4642–7.
30. Friedman LS, Richter ED. Relationship between conflicts of interest and research results. J Gen Intern
Med 2004; 356:51–6.
31. Tibau A, Bedard PL, Srikanthan A, Ethier JL, Vera-Badillo FE, Templeton AJ, et al. Author financial con-
flicts of interest, industry funding, and clinical practice guidelines for anticancer drugs. J Clin Oncol.
2015 Jan 1; 33(1):100–6. https://doi.org/10.1200/JCO.2014.57.8898 PMID: 25385736
32. Ahn R, Woodbridge A, Abraham A, Saba S, Korenstein D, Madden E,et al. Financial ties of principal
investigators and randomized controlled trial outcomes: cross sectional study. BMJ. 2017 Jan 17; 356.
33. Koo TK, Li MY. 2016. A guideline of selecting and reporting intraclass correlation coefficients for reliabil-
ity research. J Chiropr Med. 2016; 15(2):155–163. https://doi.org/10.1016/j.jcm.2016.02.012 PMID:
27330520
34. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assess-
ment instruments in psychology. Psychol. Assess. 1994; 6(4):284.
35. Fleiss JL. Design and analysis of clinical experiments. 2011; John Wiley & Sons.
36. Lundh A, Barbateskovic M, Hróbjartsson A, Gøtzsche PC. Correction: Conflicts of Interest at Medical Journals: The Influence of Industry-Supported Randomised Trials on Journal Impact Factors and Reve-
nue–Cohort Study. PLoS medicine. 2011 Feb; 8(2).
37. Ho J, Tumkaya T, Aryal S, Choi H, Claridge-Chang A. 2019. Moving beyond P values: data analysis
with estimation graphics. Nat Methods. 201; 16,:565–566 https://doi.org/10.1038/s41592-019-0470-3
PMID: 31217592
38. Efron B, Tibshirani RJ. An introduction to the bootstrap. 1994; CRC press.
39. Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor
Quant Methods Psychol. 2012; 8(1):23. https://doi.org/10.20982/tqmp.08.1.p023 PMID: 22833776
40. Cain DM, Loewenstein G, Moore DA. The dirt on coming clean: Perverse effects of disclosing conflicts
of interest. J Leg Stud. 2005; 34(1):1–25.
41. Loewenstein G, Cain DM, Sah S. The limits of transparency: Pitfalls and potential of disclosing conflicts
of interest. Am Econ Rev. 2011; 101(3):423–28.
42. Aneja A, Esquitin R, Shah K, et al. Authors’ self-declared financial conflicts of interest do not impact the
results of major cardiovascular trials. J Am Coll Cardiol 2013; 356:1137–43.
43. Pang WK, Yeter KC, Torralba KD, Spencer HJ, Khan NA. Financial conflicts of interest and their associ-
ation with outcome and quality of fibromyalgia drug therapy randomized controlled trials. Int J Rheum
Dis 2015; 356:606–15.
44. Khan NA, Nguyen CL, Khawar T, Spencer H, Torralba KD. Association of author’s financial conflict of
interest with characteristics and outcome of rheumatoid arthritis randomized controlled trials. Rheuma-
tology. 2019 May 1; 58(5):776–85. https://doi.org/10.1093/rheumatology/key368 PMID: 30535001
PLOS ONE Relationships among commercial practices and author conflicts of interest in biomedical publishing
PLOS ONE | https://doi.org/10.1371/journal.pone.0236166 July 24, 2020 11 / 11
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
