A model-based meta-analysis of the influence of factors that impact adherence to medications

2014 ◽  
Vol 40 (1) ◽  
pp. 24-31 ◽  
Author(s):  
P. Assawasuwannakit ◽  
R. Braund ◽  
S. B. Duffull
Keyword(s):  
Meta Analysis ◽  
Model Based

scholarly journals A model-based correction for outcome reporting bias in meta-analysis

Biostatistics ◽  
2013 ◽  
Vol 15 (2) ◽  
pp. 370-383 ◽  
Author(s):  
J. Copas ◽  
K. Dwan ◽  
J. Kirkham ◽  
P. Williamson
Keyword(s):  
Meta Analysis ◽  
Reporting Bias ◽  
Outcome Reporting Bias ◽  
Outcome Reporting ◽  
Model Based

scholarly journals Bayesian splines versus fractional polynomials in network meta-analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Andreas Heinecke ◽  
Marta Tallarita ◽  
Maria De Iorio
Keyword(s):  
Meta Analysis ◽  
Indirect Comparison ◽  
Real Data ◽  
Multiple Time ◽  
Computationally Efficient ◽  
Fractional Polynomials ◽  
Time Points ◽  
Model Based ◽  
Multiple Treatments ◽  
Indirect Comparisons

Abstract Background Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. Methods In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. Results We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. Conclusions The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Determinants of Technology Acceptance: Two Model-Based Meta-Analytic Reviews

2020 ◽  
pp. 107769902095240 ◽  
Author(s):  
Guangchao Charles Feng ◽  
Xianglin Su ◽  
Zhiliang Lin ◽  
Yiru He ◽  
Nan Luo ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Meta Analysis ◽  
Model Fit ◽  
Theoretical Frameworks ◽  
Model Based ◽  
Fit Testing ◽  
Central Interest ◽  
Acceptance Model ◽  
Analytical Approaches

Examining the determinants of technology acceptance has been a central interest across disciplines. The technology acceptance model (TAM) and its variants and extensions are the most popular theoretical frameworks in this line of research. Two model-based meta-analytical approaches, that is, meta-meta-analysis and conventional meta-analysis, are used to pool the correlations and to test the path relationships among the variables of the TAM. We find that the extended TAM, which we term the TAM Plus, prevails in the model fit testing and that the results of the pooled correlations and path coefficients estimated using the meta-meta-analysis and meta-analysis are generally consistent.

scholarly journals A Model-Based Meta-analysis to Compare Efficacy and Tolerability of Tramadol and Tapentadol for the Treatment of Chronic Non-Malignant Pain

2014 ◽  
Vol 3 (1) ◽  
pp. 31-44 ◽  
Author(s):  
François Mercier ◽  
Laurent Claret ◽  
Klaas Prins ◽  
René Bruno
Keyword(s):  
Meta Analysis ◽  
Model Based

A model-based meta-analysis for estimating species-specific wood density and identifying potential sources of variation

2013 ◽  
Vol 102 (1) ◽  
pp. 194-208 ◽  
Author(s):  
Kiona Ogle ◽  
Sharmila Pathikonda ◽  
Karla Sartor ◽  
Jeremy W. Lichstein ◽  
Jeanne L. D. Osnas ◽  
...  
Keyword(s):  
Wood Density ◽  
Meta Analysis ◽  
Model Based ◽  
Potential Sources ◽  
Sources Of Variation ◽  
Species Specific
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