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Lu Qin; Shishun Zhao; Wenlai Guo; Tiejun Tong; Ke Yang – Research Synthesis Methods, 2024
The application of network meta-analysis is becoming increasingly widespread, and for a successful implementation, it requires that the direct comparison result and the indirect comparison result should be consistent. Because of this, a proper detection of inconsistency is often a key issue in network meta-analysis as whether the results can be…
Descriptors: Meta Analysis, Network Analysis, Bayesian Statistics, Comparative Analysis
de Jong, Valentijn M. T.; Campbell, Harlan; Maxwell, Lauren; Jaenisch, Thomas; Gustafson, Paul; Debray, Thomas P. A. – Research Synthesis Methods, 2023
A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate…
Descriptors: Classification, Meta Analysis, Bayesian Statistics, Evaluation Methods
Jona Lilienthal; Sibylle Sturtz; Christoph Schürmann; Matthias Maiworm; Christian Röver; Tim Friede; Ralf Bender – Research Synthesis Methods, 2024
In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are…
Descriptors: Bayesian Statistics, Meta Analysis, Health Sciences, Technology
Caspar J. Van Lissa; Eli-Boaz Clapper; Rebecca Kuiper – Research Synthesis Methods, 2024
The product Bayes factor (PBF) synthesizes evidence for an informative hypothesis across heterogeneous replication studies. It can be used when fixed- or random effects meta-analysis fall short. For example, when effect sizes are incomparable and cannot be pooled, or when studies diverge significantly in the populations, study designs, and…
Descriptors: Hypothesis Testing, Evaluation Methods, Replication (Evaluation), Sample Size
Van Lissa, Caspar J.; van Erp, Sara; Clapper, Eli-Boaz – Research Synthesis Methods, 2023
When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…
Descriptors: Bayesian Statistics, Regression (Statistics), Maximum Likelihood Statistics, Meta Analysis
Piepho, Hans-Peter; Madden, Laurence V. – Research Synthesis Methods, 2022
Network meta-analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within-study information on treatment contrasts and integrates this over a network of studies. One…
Descriptors: Medical Research, Meta Analysis, Networks, Drug Therapy
Okada, Kensuke – Research Synthesis Methods, 2015
This paper proposes a new method to evaluate informative hypotheses for meta-analysis of Cronbach's coefficient alpha using a Bayesian approach. The coefficient alpha is one of the most widely used reliability indices. In meta-analyses of reliability, researchers typically form specific informative hypotheses beforehand, such as "alpha of…
Descriptors: Correlation, Bayesian Statistics, Meta Analysis, Hypothesis Testing