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Relevant bibliographies by topics / Mixed meta-model

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Journal articles

Academic literature on the topic 'Mixed meta-model'

Author: Grafiati

Published: 10 November 2022

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Journal articles on the topic "Mixed meta-model"

1

Kalaian, Hripsime A., and Stephen W. Raudenbush. "A multivariate mixed linear model for meta-analysis." Psychological Methods 1, no. 3 (1996): 227–35. http://dx.doi.org/10.1037/1082-989x.1.3.227.

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LeBeau, Brandon, Yoon Ah Song, and Wei Cheng Liu. "Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis." SAGE Open 8, no. 4 (2018): 215824401882038. http://dx.doi.org/10.1177/2158244018820380.

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This meta-analysis attempts to synthesize the Monte Carlo (MC) literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC simulation conditions will be coded to serve as moderator variables. The type I error rate for the f

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3

Stram, Daniel O. "Meta-Analysis of Published Data Using a Linear Mixed-Effects Model." Biometrics 52, no. 2 (1996): 536. http://dx.doi.org/10.2307/2532893.

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Barbillon, Pierre, Célia Barthélémy, and Adeline Samson. "Parameter estimation of complex mixed models based on meta-model approach." Statistics and Computing 27, no. 4 (2016): 1111–28. http://dx.doi.org/10.1007/s11222-016-9674-x.

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5

Shim, Minjung, Burke Johnson, Joke Bradt, and Susan Gasson. "A Mixed Methods–Grounded Theory Design for Producing More Refined Theoretical Models." Journal of Mixed Methods Research 15, no. 1 (2020): 61–86. http://dx.doi.org/10.1177/1558689820932311.

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Current literature lacks explication of how traditional grounded theory and mixed methods–grounded theory (MM-GT) are similar/different and specific explication of how to construct MM-GT designs—our purpose is to do this. We illustrate the design process using a published study. Exploratory Phase 1 involves creation of a formative–theoretical model based on multiple implicit or explicit models identified in the literature, which are then combined into a single model using meta-modeling integration. Also, in Phase 1, a traditional grounded theory is developed “independently” using interview dat

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6

Nikoloulopoulos, Aristidis K. "Hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests." Statistical Methods in Medical Research 27, no. 8 (2016): 2540–53. http://dx.doi.org/10.1177/0962280216682376.

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Copula mixed models for trivariate (or bivariate) meta-analysis of diagnostic test accuracy studies accounting (or not) for disease prevalence have been proposed in the biostatistics literature to synthesize information. However, many systematic reviews often include case-control and cohort studies, so one can either focus on the bivariate meta-analysis of the case-control studies or the trivariate meta-analysis of the cohort studies, as only the latter contains information on disease prevalence. In order to remedy this situation of wasting data we propose a hybrid copula mixed model via a com

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7

Lee, Young Hoon, Yang Woo Shin, and Dug Hee Moon. "Buffer Allocation Problem Using Meta-Model in an Automotive Body Shops with Mixed-model Production." Journal of the Korean Institute of Industrial Engineers 46, no. 3 (2020): 296–308. http://dx.doi.org/10.7232/jkiie.2020.46.3.296.

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Yu, Tianwei. "Dimension reduction and mixed-effects model for microarray meta-analysis of cancer." Frontiers in Bioscience 13, no. 13 (2008): 2714. http://dx.doi.org/10.2741/2878.

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9

Singmann, Henrik, Karl Christoph Klauer, and David Kellen. "Intuitive Logic Revisited: New Data and a Bayesian Mixed Model Meta-Analysis." PLoS ONE 9, no. 4 (2014): e94223. http://dx.doi.org/10.1371/journal.pone.0094223.

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10

Vogelgesang, Felicitas, Marc Dewey, and Peter Schlattmann. "The Evaluation of Bivariate Mixed Models in Meta-analyses of Diagnostic Accuracy Studies with SAS, Stata and R." Methods of Information in Medicine 57, no. 03 (2018): 111–19. http://dx.doi.org/10.3414/me17-01-0021.

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Summary Background: Meta-analyses require a thoroughly planned procedure to obtain unbiased overall estimates. From a statistical point of view not only model selection but also model implementation in the software affects the results. Objectives: The present simulation study investigates the accuracy of different implementations of general and generalized bivariate mixed models in SAS (using proc mixed, proc glimmix and proc nlmixed), Stata (using gllamm, xtmelogit and midas) and R (using reitsma from package mada and glmer from package lme4). Both models incorporate the relationship between

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