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Reporting bias in medical research - a narrative review.

ABSTRACT: Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles.We identified reporting bias in 40 indications comprising around 50 different pharmacological, surgical (e.g. vacuum-assisted closure therapy), diagnostic (e.g. ultrasound), and preventive (e.g. cancer vaccines) interventions. Regarding pharmacological interventions, cases of reporting bias were, for example, identified in the treatment of the following conditions: depression, bipolar disorder, schizophrenia, anxiety disorder, attention-deficit hyperactivity disorder, Alzheimer's disease, pain, migraine, cardiovascular disease, gastric ulcers, irritable bowel syndrome, urinary incontinence, atopic dermatitis, diabetes mellitus type 2, hypercholesterolaemia, thyroid disorders, menopausal symptoms, various types of cancer (e.g. ovarian cancer and melanoma), various types of infections (e.g. HIV, influenza and Hepatitis B), and acute trauma. Many cases involved the withholding of study data by manufacturers and regulatory agencies or the active attempt by manufacturers to suppress publication. The ascertained effects of reporting bias included the overestimation of efficacy and the underestimation of safety risks of interventions.In conclusion, reporting bias is a widespread phenomenon in the medical literature. Mandatory prospective registration of trials and public access to study data via results databases need to be introduced on a worldwide scale. This will allow for an independent review of research data, help fulfil ethical obligations towards patients, and ensure a basis for fully-informed decision making in the health care system.

SUBMITTER: McGauran N

PROVIDER: S-EPMC2867979 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Reporting bias in medical research - a narrative review.

McGauran Natalie N Wieseler Beate B Kreis Julia J Schüler Yvonne-Beatrice YB Kölsch Heike H Kaiser Thomas T

Trials 20100413

Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in ar ...[more]

PMID: 20388211

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Project description:BACKGROUND:The 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 FINDINGS:In 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. CONCLUSIONS:This 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:BackgroundThe intent of plain-language resources (PLRs) reporting medical research information is to advance health literacy among the general public and enable them to participate in shared decision-making (SDM). Regulatory mandates coupled with academic and industry initiatives have given rise to an increasing volume of PLRs summarizing medical research information. However, there is significant variability in the quality, format, readability, and dissemination channels for PLRs. In this scoping review, we identify current practices, guidance, and barriers in developing and disseminating PLRs reporting medical research information to the general public including patients and caregivers. We also report on the PLR preferences of these intended audiences.MethodsA literature search of three bibliographic databases (PubMed, EMBASE, Web of Science) and three clinical trial registries (NIH, EMA, ISRCTN registry) was performed. Snowball searches within reference lists of primary articles were added. Articles with PLRs or reporting topics related to PLRs use and development available between January 2017 and June 2023 were identified. Evidence mapping and synthesis were used to make qualitative observations. Identified PLRs were quantitatively assessed, including temporal annual trends, availability by field of medicine, language, and publisher types.ResultsA total of 9116 PLRs were identified, 9041 from the databases and 75 from clinical trial registries. The final analysis included 6590 PLRs from databases and 72 from registries. Reported barriers to PLR development included ambiguity in guidance, lack of incentives, and concerns of researchers writing for the general public. Available guidance recommendations called for greater dissemination, increased readability, and varied content formats. Patients preferred visual PLRs formats (e.g., videos, comics), which were easy to access on the internet and used short jargon-free text. In some instances, older audiences and more educated readers preferred text-only PLRs. Preferences among the general public were mostly similar to those of patients. Psychology, followed by oncology, showed the highest number of PLRs, predominantly from academia-sponsored research. Text-only PLRs were most commonly available, while graphical, digital, or online formats were less available. Preferred dissemination channels included paywall-free journal websites, indexing on PubMed, third-party websites, via email to research participants, and social media.ConclusionsThis scoping review maps current practices, recommendations, and patients' and the general public's preferences for PLR development and dissemination. The results suggest that making PLRs available to a wider audience by improving nomenclature, accessibility, and providing translations may contribute to empowerment and SDM. Minimizing variability among available guidance for PLR development may play an important role in amplifying the value and impact of these resources.

Project description:Background and objectiveTransformers, which have been widely recognized as state-of-the-art tools in natural language processing (NLP), have also come to be recognized for their value in computer vision tasks. With this increasing popularity, they have also been extensively researched in the more complex medical imaging domain. The associated developments have resulted in transformers being on par with sought-after convolution neural networks, particularly for medical image segmentation. Methods combining both types of networks have proven to be especially successful in capturing local and global contexts, thereby significantly boosting their performances in various segmentation problems. Motivated by this success, we have attempted to survey the consequential research focused on innovative transformer networks, specifically those designed to cater to medical image segmentation in an efficient manner.MethodsDatabases like Google Scholar, arxiv, ResearchGate, Microsoft Academic, and Semantic Scholar have been utilized to find recent developments in this field. Specifically, research in the English language from 2021 to 2023 was considered.Key content and findingsIn this survey, we look into the different types of architectures and attention mechanisms that uniquely improve performance and the structures that are in place to handle complex medical data. Through this survey, we summarize the popular and unconventional transformer-based research as seen through different key angles and analyze quantitatively the strategies that have proven more advanced.ConclusionsWe have also attempted to discern existing gaps and challenges within current research, notably highlighting the deficiency of annotated medical data for precise deep learning model training. Furthermore, potential future directions for enhancing transformers' utility in healthcare are outlined, encompassing strategies such as transfer learning and exploiting foundation models for specialized medical image segmentation.

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Reporting bias in medical research - a narrative review.

Publications

Reporting bias in medical research - a narrative review.

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