Journal articles: 'Mixed meta-model'

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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 (October 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 fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new MC studies will be explored.

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Stram, Daniel O. "Meta-Analysis of Published Data Using a Linear Mixed-Effects Model." Biometrics 52, no. 2 (June 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 (June 22, 2016): 1111–28. http://dx.doi.org/10.1007/s11222-016-9674-x.

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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 (June 8, 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 data. These two models are integrated into a combined/meta-model at the end of Phase 1. Confirmatory Phase 2 involves testing of the final Phase 1 meta-model using a mixed methods experiment. In Phase 3, the Phase 1 and Phase 2 results are integrated, producing the “final” meta-model. This article contributes to the field of mixed methods research by showing how to design an MM-GT study that is focused on theory development and testing.

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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 (December 21, 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 combination of the bivariate and trivariate copula mixed model for the data from the case-control studies and cohort studies, respectively. Hence, this hybrid model can account for study design and also due to its generality can deal with dependence in the joint tails. We apply the proposed hybrid copula mixed model to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma.

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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 (June 30, 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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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 (April 22, 2014): e94223. http://dx.doi.org/10.1371/journal.pone.0094223.

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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 (May 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 sensitivity and specificity – the two outcomes of interest in meta-analyses of diagnostic accuracy studies – utilizing random effects. Methods: Model performance is compared in nine meta-analytic scenarios reflecting the combination of three sizes for meta-analyses (89, 30 and 10 studies) with three pairs of sensitivity/specificity values (97%/87%; 85%/75%; 90%/93%). Results: The evaluation of accuracy in terms of bias, standard error and mean squared error reveals that all implementations of the generalized bivariate model calculate sensitivity and specificity estimates with deviations less than two percentage points. proc mixed which together with reitsma implements the general bivariate mixed model proposed by Reitsma rather shows convergence problems. The random effect parameters are in general underestimated. Conclusions: This study shows that flexibility and simplicity of model specification together with convergence robustness should influence implementation recommendations, as the accuracy in terms of bias was acceptable in all implementations using the generalized approach.

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Son, Young Kap, and Gordon J. Savage. "A Simple Explicit Meta-Model for Probabilistic Design of Dynamic Systems with Multiple Mixed Inputs." International Journal of Reliability, Quality and Safety Engineering 25, no. 03 (April 23, 2018): 1850011. http://dx.doi.org/10.1142/s0218539318500110.

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Most systems have multiple inputs that comprise of a mixture of excitations and component parameters. Excitations are different from component parameters in that they are always functions of time. In mechanical systems, these include applied forces, applied displacements, system settings, systems configurations and operating conditions. It would be convenient to include multiple excitations and multiple component parameters in a meta-model to take advantage of the inherent computation speed needed for timely probability-based design optimization. In the development of the meta-model in this paper, we treat the component parameters in the same manner as the excitations and thus, in both cases, form time-sampled vectors. A design-of-experiments training regime creates a single input matrix, and using the mechanistic model, a single output matrix. Finally, a simple, explicit, meta-model is developed that turns an arbitrary vector of contiguous multiple excitations and multiple component parameters into the corresponding output vector (herein, the response). The approach provides an appealing and efficient solution to the multiple, mixed input problem, and in addition, requires only off-the-shelf computer software. The efficacy of the meta-model is shown through probability-based design optimization (PBDO) of a tire-wheel assembly, modelled as a mass-spring-damper system with nonlinear hysteresis, under a combination of practical inputs.

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Nikoloulopoulos, Aristidis K. "A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests." Statistical Methods in Medical Research 28, no. 10-11 (September 26, 2018): 3286–300. http://dx.doi.org/10.1177/0962280218796685.

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For a particular disease, there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalised linear mixed model (GLMM) has been recently proposed to joint meta-analyse and compare two diagnostic tests. We propose a D-vine copula mixed model for joint meta-analysis and comparison of two diagnostic tests. Our general model includes the quadrivariate GLMM as a special case and can also operate on the original scale of sensitivities and specificities. The method allows the direct calculation of sensitivity and specificity for each test, as well as the parameters of the summary receiver operator characteristic (SROC) curve, along with a comparison between the SROCs of each test. Our methodology is demonstrated with an extensive simulation study and illustrated by meta-analysing two examples where two tests for the diagnosis of a particular disease are compared. Our study suggests that there can be an improvement on GLMM in fit to data since our model can also provide tail dependencies and asymmetries.

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Willis, Brian H., Mohammed Baragilly, and Dyuti Coomar. "Maximum likelihood estimation based on Newton–Raphson iteration for the bivariate random effects model in test accuracy meta-analysis." Statistical Methods in Medical Research 29, no. 4 (June 11, 2019): 1197–211. http://dx.doi.org/10.1177/0962280219853602.

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A bivariate generalised linear mixed model is often used for meta-analysis of test accuracy studies. The model is complex and requires five parameters to be estimated. As there is no closed form for the likelihood function for the model, maximum likelihood estimates for the parameters have to be obtained numerically. Although generic functions have emerged which may estimate the parameters in these models, they remain opaque to many. From first principles we demonstrate how the maximum likelihood estimates for the parameters may be obtained using two methods based on Newton–Raphson iteration. The first uses the profile likelihood and the second uses the Observed Fisher Information. As convergence may depend on the proximity of the initial estimates to the global maximum, each algorithm includes a method for obtaining robust initial estimates. A simulation study was used to evaluate the algorithms and compare their performance with the generic generalised linear mixed model function glmer from the lme4 package in R before applying them to two meta-analyses from the literature. In general, the two algorithms had higher convergence rates and coverage probabilities than glmer. Based on its performance characteristics the method of profiling is recommended for fitting the bivariate generalised linear mixed model for meta-analysis.

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Onwuegbuzie, Anthony John, and Kathleen M. T. Collins. "The Role of Bronfenbrennerâ€™s Ecological Systems Theory in Enhancing Interpretive Consistency in Mixed Research." INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION METHODOLOGY 5, no. 2 (August 30, 2014): 651–61. http://dx.doi.org/10.24297/ijrem.v5i2.3910.

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One of the nine major threats to legitimation (i.e., the degree that integration of findings leads to credible and defensible meta-inferences) is sample legitimation integration (Onwuegbuzie & Johnson, 2006). Addressing this form of legitimation requires the researcher to maintain interpretive consistency between the selected sampling design and the inferences made from the ensuing findings.Â To facilitate researchersâ€™ efforts to address interpretive consistency, in this article, we provide a meta-sampling framework that is structured in accordance to the dimensions of Bronfenbrennerâ€™s (1979) ecological systems model. In this meta-framework, the four dimensions of the model are juxtaposed to various types of generalizations, sampling-based considerations, and mixed sampling criteria. Application of this inclusive framework is appropriate for the conduct of quantitative, qualitative, and mixed research.Â

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de-Miguel, Sergio, Lauri Mehtätalo, and Ali Durkaya. "Developing generalized, calibratable, mixed-effects meta-models for large-scale biomass prediction." Canadian Journal of Forest Research 44, no. 6 (June 2014): 648–56. http://dx.doi.org/10.1139/cjfr-2013-0385.

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Large-scale prediction of forest biomass is of interest for forest science, ecology, and issues related to climate change. Previous research has attempted to provide allometric models suitable for large-scale biomass prediction using different methods. We present a new approach for meta-analysis of existing biomass equations using mixed-effects modelling on simulated data. The resulting generalized meta-models can be calibrated for local conditions. This meta-analytical approach allows for directly benefiting from previous research to minimize data collection and properly take into account the unknown differences between different locations within large areas. The approach is demonstrated by developing pan-Mediterranean mixed-effects meta-models for Pinus brutia Ten. The fixed part of the meta-models enables sound aboveground biomass predictions throughout practically the full native range of the species. Significant improvement in the predictive performance can be further gained by using small local datasets for model calibration. The calibration procedure for location-specific biomass prediction is based on best linear unbiased predictor of random effects. The predictive performance of the meta-models under different sampling strategies is validated in an independent dataset. The results show that mixed-effects meta-models may enable accurate and robust large-scale biomass predictions. Calibration for specific locations based on minimal data collection effort performs better than fitting location-specific equations based on much larger samples. The advantages of mixed-effects meta-models are of interest not only for further biomass-related research and applications, but also for other modelling disciplines within forest science.

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Jain, Savita, Kanchan Jain, and Suresh K. Sharma. "Trivariate generalized non linear mixed model with transformations for meta analysis of diagnostic accuracy." Model Assisted Statistics and Applications 13, no. 1 (February 5, 2018): 73–83. http://dx.doi.org/10.3233/mas-170419.

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Tu, Yu-Kang. "Linear mixed model approach to network meta-analysis for continuous outcomes in periodontal research." Journal of Clinical Periodontology 42, no. 2 (February 2015): 204–12. http://dx.doi.org/10.1111/jcpe.12362.

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Doebler, Philipp, Heinz Holling, and Dankmar Böhning. "A mixed model approach to meta-analysis of diagnostic studies with binary test outcome." Psychological Methods 17, no. 3 (September 2012): 418–36. http://dx.doi.org/10.1037/a0028091.

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Fontalvo, Mauricio Orozco, Victor Cantillo Maza, and Pablo Miranda. "A Meta-Heuristic Approach to a Strategic Mixed Inventory-Location Model: Formulation and Application." Transportation Research Procedia 25 (2017): 729–46. http://dx.doi.org/10.1016/j.trpro.2017.05.454.

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Sul, Jae Hoon, Buhm Han, Chun Ye, Ted Choi, and Eleazar Eskin. "Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches." PLoS Genetics 9, no. 6 (June 13, 2013): e1003491. http://dx.doi.org/10.1371/journal.pgen.1003491.

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Rosenberger, Kristine J., Haitao Chu, and Lifeng Lin. "Empirical comparisons of meta-analysis methods for diagnostic studies: a meta-epidemiological study." BMJ Open 12, no. 5 (May 2022): e055336. http://dx.doi.org/10.1136/bmjopen-2021-055336.

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ObjectivesSeveral methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (ROC) estimates. Nevertheless, the bivariate LMM makes potentially unrealistic assumptions (ie, normality of within-study estimates), which could be avoided by the bivariate generalised linear mixed model (GLMM). This article aims at investigating the real-world performance of the bivariate LMM and GLMM using meta-analyses of diagnostic studies from the Cochrane Library.MethodsWe compared the bivariate LMM and GLMM using the relative differences in the overall sensitivity and specificity, their 95% CI widths, between-study variances, and the correlation between the (logit) sensitivity and specificity. We also explored their relationships with the number of studies, number of subjects, overall sensitivity and overall specificity.ResultsAmong the extracted 1379 meta-analyses, point estimates of overall sensitivities and specificities by the bivariate LMM and GLMM were generally similar, but their CI widths could be noticeably different. The bivariate GLMM generally produced narrower CIs than the bivariate LMM when meta-analyses contained 2–5 studies. For meta-analyses with <100 subjects or the overall sensitivities or specificities close to 0% or 100%, the bivariate LMM could produce substantially different AUCs, DORs and DOR CI widths from the bivariate GLMM.ConclusionsThe variation of estimates calls into question the appropriateness of the normality assumption within individual studies required by the bivariate LMM. In cases of notable differences presented in these methods’ results, the bivariate GLMM may be preferred.

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Xu, Peng, Gui Chen Wang, Xiao Cai, Hai Yang Shen, and Wei Xiang Jiang. "Design and optimization of high-efficiency meta-devices based on the equivalent circuit model and theory of electromagnetic power energy storage." Journal of Physics D: Applied Physics 55, no. 19 (February 14, 2022): 195303. http://dx.doi.org/10.1088/1361-6463/ac4e34.

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Abstract Meta-devices with high operation efficiency to control electromagnetic waves are of great interest in a variety of applications. In this paper, we propose a general design method to achieve maximum operating efficiency for different-function meta-devices. The method is based on the equivalent circuit model and the theory of electromagnetic energy storage. To demonstrate its validity, three different kinds of functional meta-devices, including a beam deflection meta-array, circular polarization microwave absorber and linear-to-circular polarization converter, are presented using the proposed method. The method can be used for arbitrarily-structured meta-devices, three-dimensional (3D) all-metal structures or planar dielectric-metal-mixed structures. Additionally, such a method may be applied to design meta-devices for any polarized waves and can be expanded to other operating frequency bands.

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Böhning, D. "Meta-Analysis." Methods of Information in Medicine 44, no. 01 (2005): 127–35. http://dx.doi.org/10.1055/s-0038-1633931.

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Summary Objectives: This contribution provides a unifying concept for meta-analysis integrating the handling of unobserved heterogeneity, study covariates, publication bias and study quality. It is important to consider these issues simultaneously to avoid the occurrence of artifacts, and a method for doing so is suggested here. Methods: The approach is based upon the meta-likelihood in combination with a general linear nonparametric mixed model, which lays the ground for all inferential conclusions suggested here. Results: The concept is illustrated at hand of a meta-analysis investigating the relationship of hormone replacement therapy and breast cancer. The phenomenon of interest has been investigated in many studies for a considerable time and different results were reported. In 1992 a meta-analysis by Sillero-Arenas et al. [1] concluded a small, but significant overall effect of 1.06 on the relative risk scale. Using the meta-likelihood approach it is demonstrated here that this meta-analysis is due to considerable unobserved heterogeneity. Furthermore, it is shown that new methods are available to model this heterogeneity successfully. It is argued further to include available study covariates to explain this heterogeneity in the meta-analysis at hand. Conclusions: The topic of HRT and breast cancer has again very recently become an issue of public debate, when results of a large trial investigating the health effects of hormone replacement therapy were published indicating an increased risk for breast cancer (risk ratio of 1.26). Using an adequate regression model in the previously published meta-analysis an adjusted estimate of effect of 1.14 can be given which is considerably higher than the one published in the meta-analysis of Sillero-Arenas et al. [1]. In summary, it is hoped that the method suggested here contributes further to a good meta-analytic practice in public health and clinical disciplines.

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Rotolo, Federico, Xavier Paoletti, Tomasz Burzykowski, Marc Buyse, and Stefan Michiels. "A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses." Statistical Methods in Medical Research 28, no. 1 (July 6, 2017): 170–83. http://dx.doi.org/10.1177/0962280217718582.

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Surrogate endpoints are often used in clinical trials instead of well-established hard endpoints for practical convenience. The meta-analytic approach relies on two measures of surrogacy: one at the individual level and one at the trial level. In the survival data setting, a two-step model based on copulas is commonly used. We present a new approach which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. We fit this model using auxiliary mixed Poisson models. We study via simulations the operating characteristics of this mixed Poisson approach as compared to the two-step copula approach. We illustrate the application of the methods on two individual patient data meta-analyses in gastric cancer, in the advanced setting (4069 patients from 20 randomized trials) and in the adjuvant setting (3288 patients from 14 randomized trials).

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Cheung, Mike W. L. "A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling." Psychological Methods 13, no. 3 (2008): 182–202. http://dx.doi.org/10.1037/a0013163.

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Musekiwa, Alfred, Samuel O. M. Manda, Henry G. Mwambi, and Ding-Geng Chen. "Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model." PLOS ONE 11, no. 10 (October 31, 2016): e0164898. http://dx.doi.org/10.1371/journal.pone.0164898.

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Bellavance, François, Georges Dionne, and Martin Lebeau. "The value of a statistical life: A meta-analysis with a mixed effects regression model." Journal of Health Economics 28, no. 2 (March 2009): 444–64. http://dx.doi.org/10.1016/j.jhealeco.2008.10.013.

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Hoyer, Annika, and Oliver Kuss. "Meta-analysis for the comparison of two diagnostic tests to a common gold standard: A generalized linear mixed model approach." Statistical Methods in Medical Research 27, no. 5 (August 2, 2016): 1410–21. http://dx.doi.org/10.1177/0962280216661587.

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Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest are the differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across single studies and between the two tests. We propose statistical models with a quadrivariate response (where sensitivity of test 1, specificity of test 1, sensitivity of test 2, and specificity of test 2 are the four responses) as a sensible approach to this task. Using a quadrivariate generalized linear mixed model naturally generalizes the common standard bivariate model of meta-analysis for a single diagnostic test. If information on several thresholds of the tests is available, the quadrivariate model can be further generalized to yield a comparison of full receiver operating characteristic (ROC) curves. We illustrate our model by an example where two screening methods for the diagnosis of type 2 diabetes are compared.

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Menke, J. "Bivariate Random-effects Meta-analysis of Sensitivity and Specificity with SAS PROC GLIMMIX." Methods of Information in Medicine 49, no. 01 (2010): 54–64. http://dx.doi.org/10.3414/me09-01-0001.

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Summary Objectives: Meta-analysis allows to summarize pooled sensitivities and specificities from several primary diagnostic test accuracy studies. Often these pooled estimates are indirectly obtained from a hierarchical summary receiver operating characteristics (HSROC) analysis. This article presents a generalized linear random-effects model with the new SAS PROC GLIMMIX that obtains the pooled estimates for sensitivity and specificity directly. Methods: Firstly, the formula of the bivariate random-effects model is presented in context with the literature. Then its implementation with the new SAS PROC GLIMMIX is empirically evaluated in comparison to the indirect HSROC approach, utilizing the published 2 x 2 count data of 50 meta-analyses. Results: According to the empirical evaluation the meta-analytic results from the bivariate GLIMMIX approach are nearly identical to the results from the indirect HSROC approach. Conclusions: A generalized linear mixed model with PROC GLIMMIX offers a straightforward method for bivariate random-effects meta-analysis of sensitivity and specificity.

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Xu, Xiru, Woruo Ye, Hanqing Chen, Ming Liu, Weimin Jiang, and Zhuyuan Fang. "Association of endothelial nitric oxide synthase intron 4a/b gene polymorphisms and hypertension: a systematic review and meta-analysis." Journal of International Medical Research 49, no. 11 (November 2021): 030006052097923. http://dx.doi.org/10.1177/0300060520979230.

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Objective We conducted meta-analysis of relevant case-control trials to determine the association between endothelial nitric oxide synthase (eNOS) intron 4a/b gene polymorphisms and hypertension susceptibility. Methods We searched the PubMed, Cochrane, and Embase databases using relevant keywords and reviewed pertinent literature sources. All articles published up to July 2019 were considered for inclusion. Based on the qualified studies, we performed a meta-analysis of the associations between eNOS intron 4a/b polymorphisms and the risk of hypertension. Results Fourteen studies were included in this meta-analysis, including 3344 cases and 3377 controls. The eNOS intron 4a/b locus was significantly associated with increased susceptibility to hypertension (including essential hypertension) in the overall population, according to dominant, allelic, homozygote, heterozygote, and regressive models, in the mixed population according to the regressive model, and in Caucasians according to the dominant, allelic, heterozygote, and regressive models. The eNOS intron 4a/b locus was also significantly associated with increased susceptibility to essential hypertension in the mixed population according to the heterozygote model. Conclusion eNOS intron 4a/b gene polymorphisms increase susceptibility to hypertension, including essential hypertension.

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Sharma, Aswant Kumar, and Dhanesh Kumar Sambariya. "Mixed Method for Model Order Reduction Using Meta Heuristic Harris Hawk and Routh Hurwitz Array Technique." Indian Journal of Science and Technology 14, no. 28 (July 25, 2021): 2380–90. http://dx.doi.org/10.17485/ijst/v14i28.1054.

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Zell, Jürgen, Gerald Kändler, and Marc Hanewinkel. "Predicting constant decay rates of coarse woody debris—A meta-analysis approach with a mixed model." Ecological Modelling 220, no. 7 (April 2009): 904–12. http://dx.doi.org/10.1016/j.ecolmodel.2009.01.020.

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López-López, José Antonio, Fulgencio Marín-Martínez, Julio Sánchez-Meca, Wim Van den Noortgate, and Wolfgang Viechtbauer. "Estimation of the predictive power of the model in mixed-effects meta-regression: A simulation study." British Journal of Mathematical and Statistical Psychology 67, no. 1 (January 8, 2013): 30–48. http://dx.doi.org/10.1111/bmsp.12002.

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Chu, Haitao, and Stephen R. Cole. "Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach." Journal of Clinical Epidemiology 59, no. 12 (December 2006): 1331–32. http://dx.doi.org/10.1016/j.jclinepi.2006.06.011.

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Sacot, Arnau, Víctor López-Ros, Anna Prats-Puig, Jesús Escosa, Jordi Barretina, and Julio Calleja-González. "Multidisciplinary Neuromuscular and Endurance Interventions on Youth Basketball Players: A Systematic Review with Meta-Analysis and Meta-Regression." International Journal of Environmental Research and Public Health 19, no. 15 (August 5, 2022): 9642. http://dx.doi.org/10.3390/ijerph19159642.

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The main aims of this systematic review with meta-analysis and meta-regression were to describe the effect of multidisciplinary neuromuscular and endurance interventions, including plyometric training, mixed strength and conditioning, HIIT basketball programs and repeated sprint training on youth basketball players considering age, competitive level, gender and the type of the intervention performed to explore a predictive model through a meta-regression analysis. A structured search was conducted following PRISMA guidelines and PICOS model in Medline (PubMed), Web of Science (WOS) and Cochrane databases. Groups of experiments were created according to neuromuscular power (vertical; NPV and horizontal; NPH) and endurance (E). Meta-analysis and sub-groups analysis were performed using a random effect model and pooled standardized mean differences (SMD). A random effects meta-regression was performed regressing SMD for the different sub-groups against percentage change for NPV and NPH. There was a significant positive overall effect of the multidisciplinary interventions on NPV, NPH and E. Sub-groups analysis indicate differences in the effects of the interventions on NPV and NPH considering age, gender, competitive level and the type of the intervention used. Considering the current data available, the meta-regression analysis suggests a good predictability of U-16 and plyometric training on jump performance. Besides, male and elite level youth basketball players had a good predictability on multidirectional speed and agility performance.

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Man, Rongzhou, and Ken J. Greenway. "Meta-analysis of understory white spruce response to release from overstory aspen." Forestry Chronicle 80, no. 6 (December 1, 2004): 694–704. http://dx.doi.org/10.5558/tfc80694-6.

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Meta-analysis was used to summarize the research results on the growth response of understory white spruce to release from overstory aspen from different studies available from published and unpublished sources. The data were screened for the suitability for meta-analysis. Treatment effect sizes were calculated using response ratio from mean cumulative increments of released and control trees since release in height, diameter, and volume and modeled using a polynomial mixed effect regression procedure. Predictor variables include linear, quadratic, and cubic components of three independent variables initial tree height, number of years after release, and residual basal area at release and their linear interactions. Models with a reasonable predictive power were developed for height, diameter, and volume response, but no significant model was identified for survival. The models developed in this study can be applied to predict the growth response of understory white spruce to release, based on the growth of unreleased control trees, initial tree height, residual basal area at release, and time since release. The individual tree prediction can be easily scaled up to stand level if residual tree density and distribution is known. Key words: meta-analysis, boreal mixedwood, mixed model, polynomial regression, response ratio, growth, survival

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Bracio, Klaudia, and Marek Szarucki. "Mixed Methods Utilisation in Innovation Management Research: A Systematic Literature Review and Meta-Summary." Journal of Risk and Financial Management 13, no. 11 (October 27, 2020): 252. http://dx.doi.org/10.3390/jrfm13110252.

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The main purpose of this article is to explore the application of mixed methods research in the innovation management sub-discipline utilizing a systematic literature review and meta-summary analysis. Regardless of the growing number of studies in innovation management there is still a lack of research that integrates and synthesizes this body of knowledge. Our review of 93 articles from Web of Science and Scopus databases, including content analysis, presents trends and research background in innovation management that use the mixed methods approach. This study addresses the inconsistencies in the literature and presents a holistic picture of what existing empirical studies have found to date. In addition, we have developed an innovation management model based on selected theoretical lenses to enable future researchers in a given area to choose the appropriate method. The results of the meta-summary show that 50.54% articles from our dataset are related to partially mixed dominant sequential methods, 12.90% fully mixed dominant sequential methods and 11.83% fully mixed dominant concurrent methods. We identified several research gaps and provided a future research avenue in the context of innovation management. The article analyzes empirical papers, enables identification of problems in the current research and identifies trends in the area of the studied phenomenon. The results on the topic of mixed methods in innovation management and used tools have indicated that this issue is still in a premature phase but with an upward trend of research development.

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Izci, Hava, Tim Tambuyzer, Krizia Tuand, Victoria Depoorter, Annouschka Laenen, Hans Wildiers, Ignace Vergote, et al. "A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data." JNCI: Journal of the National Cancer Institute 112, no. 10 (April 7, 2020): 979–88. http://dx.doi.org/10.1093/jnci/djaa050.

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Abstract Background Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data. Methods The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy. Results Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%). Conclusions Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.

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Hedges, Larry V. "Meta-Analysis." Journal of Educational Statistics 17, no. 4 (December 1992): 279–96. http://dx.doi.org/10.3102/10769986017004279.

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The use of statistical methods to combine the results of independent empirical research studies (meta-analysis) has a long history. Meta-analytic work can be divided into two traditions: tests of the statistical significance of combined results and methods for combining estimates across studies. The principal classes of combined significance tests are reviewed, and the limitations of these tests are discussed. Fixed effects approaches treat the effect magnitude parameters to be estimated as a consequence of a model involving fixed but unknown constants. Random effects approaches treat effect magnitude parameters as if they were sampled from a universe of effects and attempt to estimate the mean and variance of the hyperpopulation of effects. Mixed models incorporate both fixed and random effects. Finally, areas of current research are summarized, including methods for handling missing data, models for publication selection, models to handle studies that are not independent, and distribution-free models for random effects.

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Divsalar, Masoomeh, Reza Hassanzadeh, Iraj Mahdavi, and Nezam Mahdavi-Amiri. "Reserve Capacity of Mixed Urban Road Networks, Network Configuration and Signal Settings." International Journal of Applied Industrial Engineering 4, no. 1 (January 2017): 44–64. http://dx.doi.org/10.4018/ijaie.2017010103.

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The authors formulate the transportation mixed network design problem (MNDP) as a mixed-integer bi-level mathematical problem, based on the concept of reserve capacity. The upper level goal is to maximize the reserve capacity by signal settings at intersections, determine street direction and increase street capacities via addition of lanes. The lower level problem is a deterministic user equilibrium traffic assignment problem to minimize the user travel time. The model being non-convex, meta-heuristic methods are used to solve the problem. A hybridization of genetic algorithm with simulated annealing and a bee algorithm are proposed. Numerical examples are illustrated to verify the effectiveness of the proposed model and the algorithms.

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N Nyaga, Victoria, Marc Arbyn, and Marc Aerts. "Beta-binomial analysis of variance model for network meta-analysis of diagnostic test accuracy data." Statistical Methods in Medical Research 27, no. 8 (December 14, 2016): 2554–66. http://dx.doi.org/10.1177/0962280216682532.

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There are several generalized linear mixed models to combine direct and indirect evidence on several diagnostic tests from related but independent diagnostic studies simultaneously also known as network meta-analysis. The popularity of these models is due to the attractive features of the normal distribution and the availability of statistical software to obtain parameter estimates. However, modeling the latent sensitivity and specificity using the normal distribution after transformation is neither natural nor computationally convenient. In this article, we develop a meta-analytic model based on the bivariate beta distribution, allowing to obtain improved and direct estimates for the global sensitivities and specificities of all tests involved, and taking into account simultaneously the intrinsic correlation between sensitivity and specificity and the overdispersion due to repeated measures. Using the beta distribution in regression has the following advantages, that the probabilities are modeled in their proper scale rather than a monotonic transform of the probabilities. Secondly, the model is flexible as it allows for asymmetry often present in the distribution of bounded variables such as proportions, which is the case with sparse data common in meta-analysis. Thirdly, the model provides parameters with direct meaningful interpretation since further integration is not necessary to obtain the meta-analytic estimates.

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Burdon, Jeremy, Patrick Connolly, Nihal de Silva, Nagin Lallu, Jonathan Dixon, and Henry Pak. "A meta-analysis using a logit non-linear mixed effects model for ‘Hass’ avocado postharvest performance data." Postharvest Biology and Technology 86 (December 2013): 134–40. http://dx.doi.org/10.1016/j.postharvbio.2013.06.023.

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Holling, Heinz, Walailuck Böhning, and Dankmar Böhning. "Meta-analysis of diagnostic studies based upon SROC-curves: a mixed model approach using the Lehmann family." Statistical Modelling: An International Journal 12, no. 4 (August 2012): 347–75. http://dx.doi.org/10.1177/1471082x1201200403.

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Gu, Chi, M. Province, and D. C. Rao. "Meta-analysis of genetic linkage to quantitative trait loci with study-specific covariates: A mixed-effects model." Genetic Epidemiology 17, S1 (1999): S599—S604. http://dx.doi.org/10.1002/gepi.1370170797.

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Nguyen, Duy Vu Anh, and Ha Thi Mai Phan. "Quay cranes scheduling at mixed cargo ports cargo ports in Viet Nam." Science and Technology Development Journal 20, K6 (October 31, 2017): 29–34. http://dx.doi.org/10.32508/stdj.v20ik6.1168.

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Quay cranes are used to discharge cargo from and load onto a vessel. The throughput of a port highly depends on the efficient operations of quay cranes. In Vietnam ports, the cargo ports not only receive containers, but also other kinds of cargo. This study focuses on the scheduling problem of the quay cranes in these ports. The mathematical model is developed, and a meta-heuristic algorithm is used to solve the problems in reasonable computation time. The numerical examples are used to validate the performance of the algorithm.

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Malouff, John M., Einar B. Thorsteinsson, Sally E. Rooke, and Nicola S. Schutte. "Alcohol Involvement and the Five-Factor Model of Personality: A Meta-Analysis." Journal of Drug Education 37, no. 3 (September 2007): 277–94. http://dx.doi.org/10.2190/de.37.3.d.

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The purpose of this meta-analysis was to quantify the relationship between the Five-Factor Model of personality and alcohol involvement and to identify moderators of the relationship. The meta-analysis included 20 studies, 119 effect sizes, and 7,886 participants. Possible moderators examined included: five-factor rating type (self vs. other); study time-frame (cross sectional vs. longitudinal); sample type (treatment vs. non-treatment); type of alcohol involvement measure used; gender of the participants; and age of the participants. The meta-analysis showed alcohol involvement was associated with low conscientiousness, low agreeableness, and high neuroticism, a personality profile that: a) fits on the low end of a superordinate personality dimension that has been called self-control; and b) makes treatment difficult. Several significant moderators of effect size were found, including the following: studies of individuals in treatment for alcohol problems showed a more negative pattern of personality traits than did other studies; cross-sectional studies, but not longitudinal studies, showed a significant effect for agreeableness, perhaps suggesting that low agreeableness may have a different causal link to alcohol involvement from the other factors; mixed-sex samples tended to have lower effect sizes than single-sex samples, suggesting that mixing sexes in data analysis may obscure effects.

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Liu, Mingyu, Jian Yi, and Wenwen Tang. "Association between angiotensin converting enzyme gene polymorphism and essential hypertension: A systematic review and meta-analysis." Journal of the Renin-Angiotensin-Aldosterone System 22, no. 1 (January 2021): 147032032199507. http://dx.doi.org/10.1177/1470320321995074.

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Background:The current meta-analytic study explored the relation between ACE gene insertion/deletion (I/D), and the risk of EH by reviewing relevant trials so as to determine the association between Angiotensin Converting Enzyme (ACE) gene polymorphism and essential hypertension (EH) susceptibility.Methods:Relevant studies published before May 2019 were collected from the PubMed, Cochrane, Embase, CNKI, VANFUN, and VIP databases.Results:Fifty-seven studies involving a total of 32,862 patients were included. These studies found that ACE gene D allele was associated with higher EH susceptibility in allelic model, homozygote model, dominant model, and regressive model, and that Asian population with ACE gene D allele showed a higher EH susceptibility in all these models. Moreover, ACE gene D allele was found closely related to a higher EH susceptibility in the subgroups of HWE, NO HWE, Caucasian population, and Mixed population, with the majority being males in allelic model, homozygote model, and regressive model and the majority being females in allelic model.Conclusion:ACE gene D allele is associated with an overall higher EH susceptibility, which is confirmed in the subgroup analysis of Asian population, HWE, NO HWE, Caucasian population, and Mixed population.

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Li, Zuchuan, and Nicolas Cassar. "A mechanistic model of an upper bound on oceanic carbon export as a function of mixed layer depth and temperature." Biogeosciences 14, no. 22 (November 14, 2017): 5015–27. http://dx.doi.org/10.5194/bg-14-5015-2017.

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Abstract. Export production reflects the amount of organic matter transferred from the ocean surface to depth through biological processes. This export is in large part controlled by nutrient and light availability, which are conditioned by mixed layer depth (MLD). In this study, building on Sverdrup's critical depth hypothesis, we derive a mechanistic model of an upper bound on carbon export based on the metabolic balance between photosynthesis and respiration as a function of MLD and temperature. We find that the upper bound is a positively skewed bell-shaped function of MLD. Specifically, the upper bound increases with deepening mixed layers down to a critical depth, beyond which a long tail of decreasing carbon export is associated with increasing heterotrophic activity and decreasing light availability. We also show that in cold regions the upper bound on carbon export decreases with increasing temperature when mixed layers are deep, but increases with temperature when mixed layers are shallow. A meta-analysis shows that our model envelopes field estimates of carbon export from the mixed layer. When compared to satellite export production estimates, our model indicates that export production in some regions of the Southern Ocean, particularly the subantarctic zone, is likely limited by light for a significant portion of the growing season.

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Plow, Matthew, Shirley M. Moore, Martha Sajatovic, and Irene Katzan. "A mixed methods study of multiple health behaviors among individuals with stroke." PeerJ 5 (May 23, 2017): e3210. http://dx.doi.org/10.7717/peerj.3210.

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Background Individuals with stroke often have multiple cardiovascular risk factors that necessitate promoting engagement in multiple health behaviors. However, observational studies of individuals with stroke have typically focused on promoting a single health behavior. Thus, there is a poor understanding of linkages between healthy behaviors and the circumstances in which factors, such as stroke impairments, may influence a single or multiple health behaviors. Methods We conducted a mixed methods convergent parallel study of 25 individuals with stroke to examine the relationships between stroke impairments and physical activity, sleep, and nutrition. Our goal was to gain further insight into possible strategies to promote multiple health behaviors among individuals with stroke. This study focused on physical activity, sleep, and nutrition because of their importance in achieving energy balance, maintaining a healthy weight, and reducing cardiovascular risks. Qualitative and quantitative data were collected concurrently, with the former being prioritized over the latter. Qualitative data was prioritized in order to develop a conceptual model of engagement in multiple health behaviors among individuals with stroke. Qualitative and quantitative data were analyzed independently and then were integrated during the inference stage to develop meta-inferences. The 25 individuals with stroke completed closed-ended questionnaires on healthy behaviors and physical function. They also participated in face-to-face focus groups and one-to-one phone interviews. Results We found statistically significant and moderate correlations between hand function and healthy eating habits (r = 0.45), sleep disturbances and limitations in activities of daily living (r = − 0.55), BMI and limitations in activities of daily living (r = − 0.49), physical activity and limitations in activities of daily living (r = 0.41), mobility impairments and BMI (r = − 0.41), sleep disturbances and physical activity (r = − 0.48), sleep disturbances and BMI (r = 0.48), and physical activity and BMI (r = − 0.45). We identified five qualitative themes: (1) Impairments: reduced autonomy, (2) Environmental forces: caregivers and information, (3) Re-evaluation: priorities and attributions, (4) Resiliency: finding motivation and solutions, and (5) Negative affectivity: stress and self-consciousness. Three meta-inferences and a conceptual model described circumstances in which factors could influence single or multiple health behaviors. Discussion This is the first mixed methods study of individuals with stroke to elaborate on relationships between multiple health behaviors, BMI, and physical function. A conceptual model illustrates addressing sleep disturbances, activity limitations, self-image, and emotions to promote multiple health behaviors. We discuss the relevance of the meta-inferences in designing multiple behavior change interventions for individuals with stroke.

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Kamp, Jasper, Erik Olofsen, Thomas K. Henthorn, Monique van Velzen, Marieke Niesters, and Albert Dahan. "Ketamine Pharmacokinetics." Anesthesiology 133, no. 6 (September 30, 2020): 1192–213. http://dx.doi.org/10.1097/aln.0000000000003577.

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Background Several models describing the pharmacokinetics of ketamine are published with differences in model structure and complexity. A systematic review of the literature was performed, as well as a meta-analysis of pharmacokinetic data and construction of a pharmacokinetic model from raw data sets to qualitatively and quantitatively evaluate existing ketamine pharmacokinetic models and construct a general ketamine pharmacokinetic model. Methods Extracted pharmacokinetic parameters from the literature (volume of distribution and clearance) were standardized to allow comparison among studies. A meta-analysis was performed on studies that performed a mixed-effect analysis to calculate weighted mean parameter values and a meta-regression analysis to determine the influence of covariates on parameter values. A pharmacokinetic population model derived from a subset of raw data sets was constructed and compared with the meta-analytical analysis. Results The meta-analysis was performed on 18 studies (11 conducted in healthy adults, 3 in adult patients, and 5 in pediatric patients). Weighted mean volume of distribution was 252 l/70 kg (95% CI, 200 to 304 l/70 kg). Weighted mean clearance was 79 l/h (at 70 kg; 95% CI, 69 to 90 l/h at 70 kg). No effect of covariates was observed; simulations showed that models based on venous sampling showed substantially higher context-sensitive half-times than those based on arterial sampling. The pharmacokinetic model created from 14 raw data sets consisted of one central arterial compartment with two peripheral compartments linked to two venous delay compartments. Simulations showed that the output of the raw data pharmacokinetic analysis and the meta-analysis were comparable. Conclusions A meta-analytical analysis of ketamine pharmacokinetics was successfully completed despite large heterogeneity in study characteristics. Differences in output of the meta-analytical approach and a combined analysis of 14 raw data sets were small, indicative that the meta-analytical approach gives a clinically applicable approximation of ketamine population parameter estimates and may be used when no raw data sets are available. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

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Journal articles on the topic 'Mixed meta-model'

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