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Direct linkT. D. Stanley; Hristos Doucouliagos; Tomas Havranek – Research Synthesis Methods, 2024
We demonstrate that all meta-analyses of partial correlations are biased, and yet hundreds of meta-analyses of partial correlation coefficients (PCCs) are conducted each year widely across economics, business, education, psychology, and medical research. To address these biases, we offer a new weighted average, UWLS[subscript +3]. UWLS[subscript…
Descriptors: Meta Analysis, Correlation, Bias, Sample Size
Li, Hua; Shih, Ming-Chieh; Song, Cheng-Jie; Tu, Yu-Kang – Research Synthesis Methods, 2023
Network meta-analysis combines direct and indirect evidence to compare multiple treatments. As direct evidence for one treatment contrast may be indirect evidence for other treatment contrasts, biases in the direct evidence for one treatment contrast may affect not only the estimate for this particular treatment contrast but also estimates of…
Descriptors: Network Analysis, Meta Analysis, Bias, Evidence
Schmid, Matthias; Friede, Tim; Klein, Nadja; Weinhold, Leonie – Research Synthesis Methods, 2023
Recent years have seen the development of many novel scoring tools for disease prognosis and prediction. To become accepted for use in clinical applications, these tools have to be validated on external data. In practice, validation is often hampered by logistical issues, resulting in multiple small-sized validation studies. It is therefore…
Descriptors: Probability, Meta Analysis, Time, Test Validity
Maya B. Mathur – Research Synthesis Methods, 2024
Meta-analyses can be compromised by studies' internal biases (e.g., confounding in nonrandomized studies) as well as publication bias. These biases often operate nonadditively: publication bias that favors significant, positive results selects indirectly for studies with more internal bias. We propose sensitivity analyses that address two…
Descriptors: Meta Analysis, Attribution Theory, Publications, Bias
Guido Schwarzer; Gerta Rücker; Cristina Semaca – Research Synthesis Methods, 2024
The "LFK" index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the "LFK" index test to three standard tests for funnel plot asymmetry in settings with smaller or larger…
Descriptors: Bias, Meta Analysis, Simulation, Evaluation Methods
Bartoš, František; Maier, Maximilian; Wagenmakers, Eric-Jan; Doucouliagos, Hristos; Stanley, T. D. – Research Synthesis Methods, 2023
Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no…
Descriptors: Robustness (Statistics), Bayesian Statistics, Meta Analysis, Publications
Andrija Babic; Ognjen Barcot; Tomislav Viskovic; Frano Šaric; Aleksandar Kirkovski; Ivana Barun; Zvonimir Križanac; Roshan Arjun Ananda; Yuli Viviana Fuentes Barreiro; Narges Malih; Daiana Anne-Marie Dimcea; Josipa Ordulj; Ishanka Weerasekara; Matteo Spezia; Marija Franka Žuljevic; Jelena Šuto; Luca Tancredi; Andela Pijuk; Susanna Sammali; Veronica Iascone; Thilo Groote; Tina Poklepovic Pericic; Livia Puljak – Research Synthesis Methods, 2024
Risk of bias (RoB) assessment is essential to the systematic review methodology. The new version of the Cochrane RoB tool for randomized trials (RoB 2) was published in 2019 to address limitations identified since the first version of the tool was published in 2008 and to increase the reliability of assessments. This study analyzed the frequency…
Descriptors: Risk, Bias, Use Studies, Meta Analysis
František Bartoš; Maximilian Maier; Eric-Jan Wagenmakers; Franziska Nippold; Hristos Doucouliagos; John P. A. Ioannidis; Willem M. Otte; Martina Sladekova; Teshome K. Deresssa; Stephan B. Bruns; Daniele Fanelli; T. D. Stanley – Research Synthesis Methods, 2024
Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that…
Descriptors: Publications, Selection, Bias, Meta Analysis
Knop, Eric S.; Pauly, Markus; Friede, Tim; Welz, Thilo – Research Synthesis Methods, 2023
Analysts seldom include interaction terms in their meta-regression model, which can introduce bias if an interaction is present. We illustrate this by reanalysing a meta-regression study in acute heart failure. Based on a total of 285 studies, the 1-year mortality rate related to acute heart failure is considered and the connection to the…
Descriptors: Regression (Statistics), Meta Analysis, Death, Heart Disorders
Stanley, T. D.; Doucouliagos, Hristos; Ioannidis, John P. A.; Carter, Evan C. – Research Synthesis Methods, 2021
We introduce and evaluate three tests for publication selection bias based on excess statistical significance (ESS). The proposed tests incorporate heterogeneity explicitly in the formulas for expected and ESS. We calculate the expected proportion of statistically significant findings in the absence of selective reporting or publication bias based…
Descriptors: Selection, Bias, Publications, Statistical Significance
Kelsey Hannah; Neal R. Haddaway; Richard A. Fuller; Tatsuya Amano – Research Synthesis Methods, 2024
Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non-English-language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non-English-language literature, we assessed factors that may predict the…
Descriptors: Language Usage, Literature Reviews, Non English Speaking, Maps
Hamza, Tasnim; Chalkou, Konstantina; Pellegrini, Fabio; Kuhle, Jens; Benkert, Pascal; Lorscheider, Johannes; Zecca, Chiara; Iglesias-Urrutia, Cynthia P.; Manca, Andrea; Furukawa, Toshi A.; Cipriani, Andrea; Salanti, Georgia – Research Synthesis Methods, 2023
In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models…
Descriptors: Meta Analysis, Regression (Statistics), Outcomes of Treatment, Research Methodology
Bobrovitz, Niklas; Noël, Kim; Li, Zihan; Cao, Christian; Deveaux, Gabriel; Selemon, Anabel; Clifton, David A.; Yanes-Lane, Mercedes; Yan, Tingting; Arora, Rahul K. – Research Synthesis Methods, 2023
Risk of bias (RoB) assessments are a core element of evidence synthesis but can be time consuming and subjective. We aimed to develop a decision rule-based algorithm for RoB assessment of seroprevalence studies. We developed the SeroTracker-RoB algorithm. The algorithm derives seven objective and two subjective critical appraisal items from the…
Descriptors: Decision Making, Algorithms, Risk, Bias
Mizutani, Shosuke; Zhou, Yi; Tian, Yu-Shi; Takagi, Tatsuya; Ohkubo, Tadayasu; Hattori, Satoshi – Research Synthesis Methods, 2023
Meta-analysis of diagnostic test accuracy (DTA) is a powerful statistical method for synthesizing and evaluating the diagnostic capacity of medical tests and has been extensively used by clinical physicians and healthcare decision-makers. However, publication bias (PB) threatens the validity of meta-analysis of DTA. Some statistical methods have…
Descriptors: Meta Analysis, Diagnostic Tests, Accuracy, Publications
Simon Šuster; Timothy Baldwin; Karin Verspoor – Research Synthesis Methods, 2024
Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of available training data. In this study, we investigate the effectiveness of generative large language…
Descriptors: Medical Research, Safety, Experimental Groups, Control Groups
