Publication bias in diagnostic imaging: conference abstracts with positive conclusions are more likely to be published

Treanor, Lee; Frank, Robert; Cherpak, Lindsay; Sharifabadi, Anahita Dehmoobad; Salameh, Jean‐Paul; Hallgrimson, Zachary; Fabiano, Nicholas; McGrath, Trevor A.; Kraaijpoel, Noémie; Yao, Jason; Korevaar, Daniël A.; Bossuyt, Patrick M.; McInnes, Matthew D. F.

doi:10.1007/s00330-019-06568-z

Cited by 25 publications

(25 citation statements)

References 37 publications

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“…"spin") in primary DTA studies, 27,32 whereby manuscripts might be rejected on the basis of having inappropriately positive conclusion. Accordingly, we observed a large proportion of submitted abstracts and manuscripts with positive conclusions, disproportionate to those with high accuracy estimates-as documented in prior work 23,33,34 which indicates an element of "spin" among submitted materials and further suggests that publication bias may be attributable to presubmission factors. This is also consistent with prior evidence that negative conclusions in imaging DTA studies were associated with longer time from study completion to submission but not from submission to Qualitative values expressed as: number of abstracts (percentage).…”

Section: Discussionsupporting

confidence: 80%

“…Sample size calculation was performed based on prior work 23,24,31 targeting a power of 80% in detecting a minimum OR of 1.2 for the primary variables (based on consensus among investigators a 20% relative difference in odds of acceptance for conference presentation or journal publication was deemed to be the smallest relevant difference). We aimed to include an approximately equal proportion of abstracts/manuscripts from each participating conference/journal.…”

Section: Sample Size Calculationmentioning

confidence: 99%

“…[19][20][21][22] Radiology conference abstracts with "positive" conclusions, but not necessarily those with higher accuracy estimates, are more likely to achieve full-text publication of the corresponding manuscripts. 23,24 Completeness of reporting in abstracts and manuscripts has also appropriately demonstrated associations with acceptance for both presentation and publication in various fields. 8,25,26 The contribution of editorial decisions to publication bias remains uncertain.…”

mentioning

confidence: 99%

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Association of Accuracy, Conclusions, and Reporting Completeness With Acceptance by Radiology Conferences and Journals

Frank

Fabiano

Hallgrimson

et al. 2022

Magnetic Resonance Imaging

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Background: Preferential publication of studies with positive findings can lead to overestimation of diagnostic test accuracy (i.e. publication bias). Understanding the contribution of the editorial process to publication bias could inform interventions to optimize the evidence guiding clinical decisions. Purpose/Hypothesis: To evaluate whether accuracy estimates, abstract conclusion positivity, and completeness of abstract reporting are associated with acceptance to radiology conferences and journals. Study Type: Meta-research.

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Section: Discussionsupporting

confidence: 80%

Section: Sample Size Calculationmentioning

confidence: 99%

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confidence: 99%

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Association of Accuracy, Conclusions, and Reporting Completeness With Acceptance by Radiology Conferences and Journals

Frank

Fabiano

Hallgrimson

et al. 2022

Magnetic Resonance Imaging

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“…Although formal testing using Egger's regression and inspecting the funnel plots showed no evidence of publication bias, we cannot exclude the possibility of this effect, as nearly all of our included studies had reported significant results. Furthermore, we excluded non-English articles and conference abstracts from our meta-analysis, and it is more likely for a nonsignificant finding to be published only as a conference abstract [ 68 ] or in a local non-English journal [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

The Accuracy of Visceral Adiposity Index for the Screening of Metabolic Syndrome: A Systematic Review and Meta-Analysis

Bijari

Jangjoo

Emami

et al. 2021

International Journal of Endocrinology

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Background and Aims. Visceral adiposity index (VAI) is a novel marker of fat distribution and function which incorporates both anthropometric and laboratory measures. Recently, several studies have suggested VAI as a screening tool for metabolic syndrome (MetS). Here, we aimed to consolidate the results of these studies by performing a systematic review and meta-analysis. Methods and Results. We searched PubMed and EMBASE online databases for eligible studies that investigated the association of VAI and MetS. After reviewing 294 records, we included 33 eligible papers with a sum of 20516 MetS and 53242 healthy participants. The risk of bias in the included studies was assessed, and the relevant data was extracted. All included studies reported a significant association between VAI and MetS screening, but were highly heterogeneous in their reported effects. We pooled the diagnostic test accuracy metrics of VAI for MetS screening and showed that it has a moderate-to-high accuracy with an area under the summary receiver operating characteristics curve of 0.847, a pooled sensitivity of 78%, and a pooled specificity of 79%. Besides, we pooled the difference in means of VAI between patients with MetS and healthy controls, revealing that VAI was 2.15 units higher in MetS patients. Conclusions. VAI is an accurate, low-cost, and widely available screening marker for MetS. However, further studies are needed to evaluate its applicability in clinical practice, determine an optimal cut-off, and identify populations that would benefit the most from it.

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“…These investigations have found that, on average, studies with statistically significant or “positive” results are more likely to be published than null or “negative” studies (Figure 1). Such an association has been observed for randomized and nonrandomized studies of interventions, 6 diagnostic test accuracy studies, 9 prognostic accuracy studies, 10 and qualitative studies 11 . Published randomized trials of health interventions also tend to have larger intervention effect estimates on average than unpublished trials, 12 which suggests that studies with smaller effects might be considered less worthy of publication.…”

Section: Introductionmentioning

confidence: 84%

Investigating and dealing with publication bias and other reporting biases in meta‐analyses of health research: A review

Page

Sterne

Higgins

et al. 2020

Research Synthesis Methods

140

116

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A P value, or the magnitude or direction of results can influence decisions about whether, when, and how research findings are disseminated. Regardless of whether an entire study or a particular study result is unavailable because investigators considered the results to be unfavorable, bias in a meta‐analysis may occur when available results differ systematically from missing results. In this article, we summarize the empirical evidence for various reporting biases that lead to study results being unavailable for inclusion in systematic reviews, with a focus on health research. These biases include publication bias and selective nonreporting bias. We describe processes that systematic reviewers can use to minimize the risk of bias due to missing results in meta‐analyses of health research, such as comprehensive searches and prospective approaches to meta‐analysis. We also outline methods that have been designed for assessing risk of bias due to missing results in meta‐analyses of health research, including using tools to assess selective nonreporting of results, ascertaining qualitative signals that suggest not all studies were identified, and generating funnel plots to identify small‐study effects, one cause of which is reporting bias. Highlights Bias in a meta‐analysis may occur when available results differ systematically from missing results. Several different tools, plots, and statistical methods have been designed for assessing risk of bias due to missing results in meta‐analyses. These include comparison of prespecified analysis plans with completed reports to detect selective nonreporting of results, consideration of qualitative signals that suggest not all studies were identified, and the use of funnel plots to identify small‐study effects, for which reporting bias is one of several causes. Information from approaches such as funnel plots and selection models is more difficult to interpret than from less subjective approaches such as detection of incompletely reported results in studies for which prespecified analysis plans were available.

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Publication bias in diagnostic imaging: conference abstracts with positive conclusions are more likely to be published

Cited by 25 publications

References 37 publications

Association of Accuracy, Conclusions, and Reporting Completeness With Acceptance by Radiology Conferences and Journals

Association of Accuracy, Conclusions, and Reporting Completeness With Acceptance by Radiology Conferences and Journals

The Accuracy of Visceral Adiposity Index for the Screening of Metabolic Syndrome: A Systematic Review and Meta-Analysis

Investigating and dealing with publication bias and other reporting biases in meta‐analyses of health research: A review

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