Dataset Information

The General Growth Tendency: A tool to improve publication trend reporting by removing record inflation bias and enabling quantitative trend analysis.

ABSTRACT: The trend of the number of publications on a research field is often used to quantify research interest and effort, but this measure is biased by general publication record inflation. This study introduces a novel metric as an unbiased and quantitative tool for trend analysis and bibliometrics. The metric was used to reanalyze reported publication trends and perform in-depth trend analyses on patent groups and a broad range of field in the life-sciences. The analyses confirmed that inflation bias frequently results in the incorrect identification of field-specific increased growth. It was shown that the metric enables a more detailed, quantitative and robust trend analysis of peer reviewed publications and patents. Some examples of the metric's uses are quantifying inflation-corrected growth in research regarding microplastics (51% ± 10%) between 2012 and 2018 and detecting inflation-corrected growth increase for transcriptomics and metabolomics compared to genomics and proteomics (Tukey post hoc p<0.0001). The developed trend-analysis tool removes inflation bias from bibliometric trend analyses. The metric improves evidence-driven decision-making regarding research effort investment and funding allocation.

SUBMITTER: Nelis JLD

PROVIDER: S-EPMC9122180 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The General Growth Tendency: A tool to improve publication trend reporting by removing record inflation bias and enabling quantitative trend analysis.

Nelis Joost L D JLD Rosas da Silva Gonçalo G Ortuño Jordi J Tsagkaris Aristeidis S AS Borremans Benny B Haslova Jana J Colgrave Michelle L ML Elliott Christopher T CT

PloS one 20220520 5

The trend of the number of publications on a research field is often used to quantify research interest and effort, but this measure is biased by general publication record inflation. This study introduces a novel metric as an unbiased and quantitative tool for trend analysis and bibliometrics. The metric was used to reanalyze reported publication trends and perform in-depth trend analyses on patent groups and a broad range of field in the life-sciences. The analyses confirmed that inflation bia ...[more]

PMID: 35594252

Similar Datasets

Project description:BackgroundThe increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making.Methodology/principal findingsIn this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies.ConclusionsThis update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

Project description:BackgroundPublication bias is a form of scientific misconduct. It threatens the validity of research results and the credibility of science. Although several tests on publication bias exist, no in-depth evaluations are available that examine which test performs best for different research settings.MethodsFour tests on publication bias, Egger's test (FAT), p-uniform, the test of excess significance (TES), as well as the caliper test, were evaluated in a Monte Carlo simulation. Two different types of publication bias and its degree (0%, 50%, 100%) were simulated. The type of publication bias was defined either as file-drawer, meaning the repeated analysis of new datasets, or p-hacking, meaning the inclusion of covariates in order to obtain a significant result. In addition, the underlying effect (β = 0, 0.5, 1, 1.5), effect heterogeneity, the number of observations in the simulated primary studies (N = 100, 500), and the number of observations for the publication bias tests (K = 100, 1,000) were varied.ResultsAll tests evaluated were able to identify publication bias both in the file-drawer and p-hacking condition. The false positive rates were, with the exception of the 15%- and 20%-caliper test, unbiased. The FAT had the largest statistical power in the file-drawer conditions, whereas under p-hacking the TES was, except under effect heterogeneity, slightly better. The CTs were, however, inferior to the other tests under effect homogeneity and had a decent statistical power only in conditions with 1,000 primary studies.DiscussionThe FAT is recommended as a test for publication bias in standard meta-analyses with no or only small effect heterogeneity. If two-sided publication bias is suspected as well as under p-hacking the TES is the first alternative to the FAT. The 5%-caliper test is recommended under conditions of effect heterogeneity and a large number of primary studies, which may be found if publication bias is examined in a discipline-wide setting when primary studies cover different research problems.

Project description:Strategies to identify and mitigate publication bias and outcome reporting bias are frequently adopted in systematic reviews of clinical interventions but it is not clear how often these are applied in systematic reviews relating to quantitative health services and delivery research (HSDR). We examined whether these biases are mentioned and/or otherwise assessed in HSDR systematic reviews, and evaluated associating factors to inform future practice. We randomly selected 200 quantitative HSDR systematic reviews published in the English language from 2007-2017 from the Health Systems Evidence database (www.healthsystemsevidence.org). We extracted data on factors that may influence whether or not authors mention and/or assess publication bias or outcome reporting bias. We found that 43% (n = 85) of the reviews mentioned publication bias and 10% (n = 19) formally assessed it. Outcome reporting bias was mentioned and assessed in 17% (n = 34) of all the systematic reviews. Insufficient number of studies, heterogeneity and lack of pre-registered protocols were the most commonly reported impediments to assessing the biases. In multivariable logistic regression models, both mentioning and formal assessment of publication bias were associated with: inclusion of a meta-analysis; being a review of intervention rather than association studies; higher journal impact factor, and; reporting the use of systematic review guidelines. Assessment of outcome reporting bias was associated with: being an intervention review; authors reporting the use of Grading of Recommendations, Assessment, Development and Evaluations (GRADE), and; inclusion of only controlled trials. Publication bias and outcome reporting bias are infrequently assessed in HSDR systematic reviews. This may reflect the inherent heterogeneity of HSDR evidence and different methodological approaches to synthesising the evidence, lack of awareness of such biases, limits of current tools and lack of pre-registered study protocols for assessing such biases. Strategies to help raise awareness of the biases, and methods to minimise their occurrence and mitigate their impacts on HSDR systematic reviews, are needed.

Project description:BackgroundPrevious studies of drug trials submitted to regulatory authorities have documented selective reporting of both entire trials and favorable results. The objective of this study is to determine the publication rate of efficacy trials submitted to the Food and Drug Administration (FDA) in approved New Drug Applications (NDAs) and to compare the trial characteristics as reported by the FDA with those reported in publications.Methods and findingsThis is an observational study of all efficacy trials found in approved NDAs for New Molecular Entities (NMEs) from 2001 to 2002 inclusive and all published clinical trials corresponding to the trials within the NDAs. For each trial included in the NDA, we assessed its publication status, primary outcome(s) reported and their statistical significance, and conclusions. Seventy-eight percent (128/164) of efficacy trials contained in FDA reviews of NDAs were published. In a multivariate model, trials with favorable primary outcomes (OR = 4.7, 95% confidence interval [CI] 1.33-17.1, p = 0.018) and active controls (OR = 3.4, 95% CI 1.02-11.2, p = 0.047) were more likely to be published. Forty-one primary outcomes from the NDAs were omitted from the papers. Papers included 155 outcomes that were in the NDAs, 15 additional outcomes that favored the test drug, and two other neutral or unknown additional outcomes. Excluding outcomes with unknown significance, there were 43 outcomes in the NDAs that did not favor the NDA drug. Of these, 20 (47%) were not included in the papers. The statistical significance of five of the remaining 23 outcomes (22%) changed between the NDA and the paper, with four changing to favor the test drug in the paper (p = 0.38). Excluding unknowns, 99 conclusions were provided in both NDAs and papers, nine conclusions (9%) changed from the FDA review of the NDA to the paper, and all nine did so to favor the test drug (100%, 95% CI 72%-100%, p = 0.0039).ConclusionsMany trials were still not published 5 y after FDA approval. Discrepancies between the trial information reviewed by the FDA and information found in published trials tended to lead to more favorable presentations of the NDA drugs in the publications. Thus, the information that is readily available in the scientific literature to health care professionals is incomplete and potentially biased.

Dataset Information

The General Growth Tendency: A tool to improve publication trend reporting by removing record inflation bias and enabling quantitative trend analysis.

Publications

The General Growth Tendency: A tool to improve publication trend reporting by removing record inflation bias and enabling quantitative trend analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets