513,356 research outputs found
Fixed Effects and Variance Components Estimation in Three-Level Meta-Analysis
Meta-analytic methods have been widely applied to education, medicine, and the social sciences. Much of meta-analytic data are hierarchically structured since effect size estimates are nested within studies, and in turn studies can be nested within level-3 units such as laboratories or investigators, and so forth. Thus, multilevel models are a natural framework for analyzing meta-analytic data. This paper discusses the application of a Fisher scoring method in two- and three-level meta-analysis that takes into account random variation at the second and at the third levels. The usefulness of the model is demonstrated using data that provide information about school calendar types. SAS proc mixed and HLM can be used to compute the estimates of fixed effects and variance components.meta-analysis, multilevel models, random effects
Integrative medicine—designing holistic conceptual model
Introduction: Integrative approaches in medicine are becoming increasingly popular due to the health promotion revival and the evidence-based clinical efficacy and cost-effectiveness of complementary and alternative medicine (CAM). Therefore, a clear definition of the “integrative medicine” realm is fundamental. In medical science, models are created for research, educational and health policy reasons.Aim: The aim is to present a holistic conceptual model of integrative medicine, created as the first stage of a mixed-methods study in an educational context.Materials and Methods: Conceptual modelling is applied after a review in PubMed with keywords: “integrative medicine,”, “concept“, “model“; targeted qualitative content analysis of the existing definitions and models of integrative medicine.Presenting the Holistic Conceptual Model of Integrative MedicineThe created model includes five building blocks: medical education, evidence-based medicine, conventional medicine, CAM, and health promotion. The model is defined according to the three characteristics of integrative medicine such as philosophy, structure, and process. Three principles are synthesized that support and would ensure the sustainability of the model—coherence; heterogeneity, equality, tolerance; efficiency and effectiveness. Conclusion: Integrative medicine is not just a combination medicine (between CAM and conventional medicine), but a highly organized system using all possible methods for the benefit of personal and public health. Today, integrative health encompasses also the health promotion idea of “One Health” and the theory of the exposome
Classification of Chenopodium Genus Populations and Species Based on Continuous and Categorical Variables
2000 Mathematics Subject Classification: 62P10, 62H30The estimation of statistical distance between populations arises in many multivariate analysis techniques. Whereas distance measures for continuous data are well developed, those for mixed discrete and continuous data are less so because of the lack of a standard model for such data. Such mixture of variables arise frequently in the field of medicine, biometry, psychology, econometrics and only comparatively few models have been developed for evaluating distance between populations. The subject of our study were data in the field of botany. The aim of the presented investigation was to apply methods for analysis of dissimilarity between 44 populations of 13 species of Ghenopodium genus,presented by 15 variables - 10 continuous and 5 categorical. The previously developed by another authors distance measures between populations presented by mixed attributes turned out not appropriate for the available data of Chenopodium genus. F or that reason a specific distance measures were applied. The matrices with distances between populations and species were used as input for Hierarchical Cluster Analysis to explore the taxonomic structure of the Chenopodium genus
Fractional Brownian motion and multivariate-t models for longitudinal biomedical data, with application to CD4 counts in HIV-patients
Longitudinal data are widely analysed using linear mixed models, with 'random slopes' models particularly common. However, when modelling, for example, longitudinal pre-treatment CD4 cell counts in HIV-positive patients, the incorporation of non-stationary stochastic processes such as Brownian motion has been shown to lead to a more biologically plausible model and a substantial improvement in model fit. In this article, we propose two further extensions. Firstly, we propose the addition of a fractional Brownian motion component, and secondly, we generalise the model to follow a multivariate-t distribution. These extensions are biologically plausible, and each demonstrated substantially improved fit on application to example data from the Concerted Action on SeroConversion to AIDS and Death in Europe study. We also propose novel procedures for residual diagnostic plots that allow such models to be assessed. Cohorts of patients were simulated from the previously reported and newly developed models in order to evaluate differences in predictions made for the timing of treatment initiation under different clinical management strategies. A further simulation study was performed to demonstrate the substantial biases in parameter estimates of the mean slope of CD4 decline with time that can occur when random slopes models are applied in the presence of censoring because of treatment initiation, with the degree of bias found to depend strongly on the treatment initiation rule applied. Our findings indicate that researchers should consider more complex and flexible models for the analysis of longitudinal biomarker data, particularly when there are substantial missing data, and that the parameter estimates from random slopes models must be interpreted with caution. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd
Investigating the uses of corporate reputation and its effects on brand segmentation, brand differentiation and brand positioning: evidence from the Taiwanese pharmaceutical industry
This study advances current knowledge on building a brand strategy that includes corporate reputation. It employs three theories – value creation, strategic resources and corporate communication – to study the uses of corporate reputation and its effect on brand segmentation, brand differentiation and brand positioning. In the context of the Taiwanese pharmaceutical industry, a sequential mixed method approach is applied and data are analyzed using PLS SEM. Findings demonstrate the relative impacts of three uses of corporate reputation (value creation, strategic resources and corporate communication) on brand image strategy (brand segmentation, brand differentiation and brand positioning) and the implications are evaluated. This study discovers that the inclusion of medicine prices is necessary and that it negatively moderates the impact of the overall uses of corporate reputation on overall brand image strategy. This research contributes empirically as one of the few that tests reputation-and-branding-building models outside the USA and Europe
Investigating the uses of corporate reputation and its effects on brand segmentation, brand differentiation and brand positioning: evidence from the Taiwanese pharmaceutical industry
This study advances current knowledge on building a brand strategy that includes corporate reputation. It employs three theories – value creation, strategic resources and corporate communication – to study the uses of corporate reputation and its effect on brand segmentation, brand differentiation and brand positioning. In the context of the Taiwanese pharmaceutical industry, a sequential mixed method approach is applied and data are analyzed using PLS SEM. Findings demonstrate the relative impacts of three uses of corporate reputation (value creation, strategic resources and corporate communication) on brand image strategy (brand segmentation, brand differentiation and brand positioning) and the implications are evaluated. This study discovers that the inclusion of medicine prices is necessary and that it negatively moderates the impact of the overall uses of corporate reputation on overall brand image strategy. This research contributes empirically as one of the few that tests reputation-and-branding-building models outside the USA and Europe
Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects
Linear Mixed Effects (LME) models have been widely applied in clustered data
analysis in many areas including marketing research, clinical trials, and
biomedical studies. Inference can be conducted using maximum likelihood
approach if assuming Normal distributions on the random effects. However, in
many applications of economy, business and medicine, it is often essential to
impose constraints on the regression parameters after taking their real-world
interpretations into account. Therefore, in this paper we extend the classical
(unconstrained) LME models to allow for sign constraints on its overall
coefficients. We propose to assume a symmetric doubly truncated Normal (SDTN)
distribution on the random effects instead of the unconstrained Normal
distribution which is often found in classical literature. With the
aforementioned change, difficulty has dramatically increased as the exact
distribution of the dependent variable becomes analytically intractable. We
then develop likelihood-based approaches to estimate the unknown model
parameters utilizing the approximation of its exact distribution. Simulation
studies have shown that the proposed constrained model not only improves
real-world interpretations of results, but also achieves satisfactory
performance on model fits as compared to the existing model
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