Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 4 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 6 |
Descriptor
Bayesian Statistics | 6 |
Probability | 6 |
Meta Analysis | 5 |
Simulation | 5 |
Models | 4 |
Drug Therapy | 3 |
Medical Research | 3 |
Outcomes of Treatment | 3 |
Comparative Analysis | 2 |
Guidelines | 2 |
Patients | 2 |
More ▼ |
Source
Research Synthesis Methods | 6 |
Author
Baio, Gianluca | 1 |
Carlin, Bradley P. | 1 |
Chu, Haitao | 1 |
Friede, Tim | 1 |
Günhan, Burak Kürsad | 1 |
Haitao Chu | 1 |
Heath, Anna | 1 |
Held, Leonhard | 1 |
Holling, Heinz | 1 |
Hong, Hwanhee | 1 |
Jansen, Katrin | 1 |
More ▼ |
Publication Type
Journal Articles | 6 |
Reports - Research | 5 |
Information Analyses | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Jansen, Katrin; Holling, Heinz – Research Synthesis Methods, 2023
In meta-analyses of rare events, it can be challenging to obtain a reliable estimate of the pooled effect, in particular when the meta-analysis is based on a small number of studies. Recent simulation studies have shown that the beta-binomial model is a promising candidate in this situation, but have thus far only investigated its performance in a…
Descriptors: Bayesian Statistics, Meta Analysis, Probability, Simulation
Jennifer L. Proper; Haitao Chu; Purvi Prajapati; Michael D. Sonksen; Thomas A. Murray – Research Synthesis Methods, 2024
Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not…
Descriptors: Network Analysis, Meta Analysis, Prediction, Drug Therapy
Remiro-Azócar, Antonio; Heath, Anna; Baio, Gianluca – Research Synthesis Methods, 2022
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate…
Descriptors: Patients, Medical Research, Comparative Analysis, Outcomes of Treatment
Held, Leonhard; Matthews, Robert; Ott, Manuela; Pawel, Samuel – Research Synthesis Methods, 2022
It is now widely accepted that the standard inferential toolkit used by the scientific research community--null-hypothesis significance testing (NHST)--is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects…
Descriptors: Bayesian Statistics, Statistical Inference, Hypothesis Testing, Credibility
Günhan, Burak Kürsad; Röver, Christian; Friede, Tim – Research Synthesis Methods, 2020
Meta-analyses of clinical trials targeting rare events face particular challenges when the data lack adequate numbers of events for all treatment arms. Especially when the number of studies is low, standard random-effects meta-analysis methods can lead to serious distortions because of such data sparsity. To overcome this, we suggest the use of…
Descriptors: Meta Analysis, Medical Research, Drug Therapy, Bayesian Statistics
Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P. – Research Synthesis Methods, 2016
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two…
Descriptors: Bayesian Statistics, Meta Analysis, Outcomes of Treatment, Comparative Analysis