Meta-analytic structural equation modeling with moderating effects on SEM parameters

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and provides measures of overall fit of the hypothesized model on meta-analytic data. We propose a novel MASEM method, one-stage MASEM, which is better suitable to explain study-level heterogeneity than existing methods. One-stage MASEM allows researchers to incorporate continuous or categorical moderators into the MASEM, in which any parameter in the structural equation model (e.g., path coefficients and factor loadings) can be modeled by the moderator variable, while the method does not require complete data for the primary studies included in the meta-analysis. We illustrate the new method on two real data sets, evaluate its empirical performance via a computer simulation study, and provide user-friendly R-functions and annotated syntax to assist researchers in applying one-stage MASEM. We close the article by presenting several future research directions

Meta‐analytic structural equation modeling made easy: A tutorial and web application for one‐stage MASEM

Meta‐analytic structural equation modeling (MASEM) refers to fitting structural equation models (SEMs) (such as path models or factor models) to meta‐analytic data. Currently, fitting MASEMs may be challenging for researchers that are not accustomed to working with R software and packages. Therefore, we developed webMASEM; a web application for MASEM. This app implements the one‐stage MASEM approach, and allows users to apply MASEM in a user‐friendly way. The aim of this article is to provide a tutorial on one‐stage MASEM and a practical guide to webMASEM. We will pay specific attention to how the data should be structured and prepared for webMASEM, because mistakes in this step may lead to faulty results without receiving an error message. The use of webMASEM is illustrated with an analysis of a meta‐analytic path model in which the path coefficients are moderated by a study‐level variable, a meta‐analytic factor model in which the factor loadings are moderated by a study‐level variable, and a meta‐analytic panel model in which the effects are moderated by a study‐level variable. All used datafiles and R scripts are available online

Determinants of the intention to consume edible insects in Brazil

Entomophagy has grown in interest as an alternative source of protein that could complement future demand for meat products. As a novel food, there are still many barriers to the adoption in western countries. Based on three models, the Theory of Planned Behavior, Expectancy Value and SPARTA, a new model is proposed. It considers key factors that could most influence consumers about their intentions, rejection and determinant behaviors regarding the extent insects such as crickets and cricket protein could replace animal protein in Brazil. Data from a sample of 404 respondents was analyzed using Structural Equation Modeling. The results reveal the positive influence of the perceived Behavioral Control and the negative influence of Subjective Norm as the main determinants of the intention to consume insects. The theoretical contribution of the research was the construction of a comprehensive and replicable converged behavioral model for application in the food innovation sector

What influences healthcare professionals' treatment preferences for older women with operable breast cancer?: an application of the discrete choice experiment

Introduction Primary endocrine therapy (PET) is used variably in the UK as an alternative to surgery for older women with operable breast cancer. Guidelines state that only patients with “significant comorbidity” or “reduced life expectancy” should be treated this way and age should not be a factor. Methods A Discrete Choice Experiment (DCE) was used to determine the impact of key variables (patient age, comorbidity, cognition, functional status, cancer stage, cancer biology) on healthcare professionals' (HCP) treatment preferences for operable breast cancer among older women. Multinomial logistic regression was used to identify associations. Results 40% (258/641) of questionnaires were returned. Five variables (age, co-morbidity, cognition, functional status and cancer size) independently demonstrated a significant association with treatment preference (p < 0.05). Functional status was omitted from the multivariable model due to collinearity, with all other variables correlating with a preference for operative treatment over no preference (p < 0.05). Only co-morbidity, cognition and cancer size correlated with a preference for PET over no preference (p < 0.05). Conclusion The majority of respondents selected treatment in accordance with current guidelines, however in some scenarios, opinion was divided, and age did appear to be an independent factor that HCPs considered when making a treatment decision in this population

Biological effects of fulvestrant on estrogen receptor positive human breast cancer: Short, medium and long-term effects based on sequential biopsies.

We report the first study of the biological effect of fulvestrant on ER positive clinical breast cancer using sequential biopsies through to progression. Thirty-two locally/systemically advanced breast cancers treated with first-line fulvestrant (250 mg/month) were biopsied at therapy initiation, 6 weeks, 6 months and progression and immunohistochemically-analyzed for Ki67, ER, EGFR and HER2 expression/signaling activity. This series showed good fulvestrant responses (duration of response [DoR] = 25.8 months; clinical benefit = 81%). Ki67 fell (p < 0.001) in 79% of tumours by 6 months and lower Ki67 at all preprogression time-points predicted for longer DoR. ER and PR significantly decreased in all tumours by 6 months (p < 0.001), with some declines in ER (serine 118) phosphorylation and Bcl-2 (p = 0.007). There were modest HER2 increases (p = 0.034, 29% tumours) and loss of any detectable EGFR phosphorylation (p = 0.024, 50% tumours) and MAP kinase (ERK1/2) phosphorylation (p = 0.019, 65% tumours) by 6 months. While ER remained low, there was some recovery of Ki67, Bcl-2 and (weakly) EGFR/MAPK activity in 45–67% patients at progression. Fulvestrant's anti-proliferative impact is related to DoR, but while commonly downregulating ER and indicators of its signaling and depleting EGFR/MAPK signaling in some patients, additional elements must determine response duration. Residual ER at fulvestrant relapse explains reported sensitivity to further endocrine therapies. Occasional modest treatment-induced HER2 and weakly detectable EGFR/HER2/MAPK signaling at relapse suggests targeting of such activity might have value alongside fulvestrant in some patients. However, unknown pathways must drive relapse in most. Ki67 has biomarker potential to predict fulvestrant outcome and as a quantitative measure of response