Publications by authors named "Katharina F Mueller"
Article Synopsis
- The objective of the review was to explore various methods for detecting and adjusting for the nonpublication of studies (dissemination bias) in meta-analyses and to check their application on empirical datasets.
- The study involved a systematic search of reputable databases, leading to the inclusion of 150 relevant articles that described a wide range of methods, from graphical techniques like funnel plots to advanced statistical methods.
- Despite identifying many approaches, the review concludes that most methods lack validation with real unpublished studies, making it challenging to recommend a specific method and highlighting the need for comprehensive literature searches and actions to improve access to research findings.
Article Synopsis
- - The study reviews influential articles on the issue of non-publication in research and aims to create a clear definition for the (non-) dissemination of findings.
- - A scoping review was conducted to gather definitions of 'publication bias' from highly cited sources, and insights from authors were compiled to draft a comprehensive document.
- - The proposed framework addresses what to consider during dissemination, who is responsible in clinical trials, and why biases in reporting may occur, aiming to guide future policies on selective publication.
Background: Systematic reviews of preclinical studies, in vivo animal experiments in particular, can influence clinical research and thus even clinical care. Dissemination bias, selective dissemination of positive or significant results, is one of the major threats to validity in systematic reviews also in the realm of animal studies. We conducted a systematic review to determine the number of published systematic reviews of animal studies until present, to investigate their methodological features especially with respect to assessment of dissemination bias, and to investigate the citation of preclinical systematic reviews on clinical research.
View Article and Find Full Text PDFBackground: Health professionals and policymakers aspire to make healthcare decisions based on the entire relevant research evidence. This, however, can rarely be achieved because a considerable amount of research findings are not published, especially in case of 'negative' results - a phenomenon widely recognized as publication bias. Different methods of detecting, quantifying and adjusting for publication bias in meta-analyses have been described in the literature, such as graphical approaches and formal statistical tests to detect publication bias, and statistical approaches to modify effect sizes to adjust a pooled estimate when the presence of publication bias is suspected.
View Article and Find Full Text PDFBackground: Meta-analyses are particularly vulnerable to the effects of publication bias. Despite methodologists' best efforts to locate all evidence for a given topic the most comprehensive searches are likely to miss unpublished studies and studies that are published in the gray literature only. If the results of the missing studies differ systematically from the published ones, a meta-analysis will be biased with an inaccurate assessment of the intervention's effects.
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