A reference guide for applications of SEM using MplusStructural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical ...terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results.Key features:Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus.Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes.Provides step-by-step instructions of model specification and estimation, as well as detail interpretation of Mplusresults.Explores different methods for sample size estimate and statistical power analysis for SEM.By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.
Category:
Trauma; Ankle
Introduction/Purpose:
Diaphyseal tibial fractures account for approximately 1.9% of all adult fractures. A recent registry review in Finland found an annual incidence of 15.6 ...per 100,000 person-years in males and 11.5 per 100,000 person-years in women. There are several studies which have demonstrated a high proportion of diaphyseal tibial fractures have ipsilateral occult posterior malleolus fractures, this ranges from 22-92.3%. Recent work by Hendrickx et al has highlighted distal third and spiral tibial shaft fracture patterns as independent predictors of occult posterior malleolus fracture.
Methods:
Objectives Our primary outcome in this study was to identify any factors that could predict articular extension in tibial shaft fractures. Study Design & Methods A retrospective review of a prospectively collected database was performed at Liverpool University Hospitals NH Foundation Trust between 1/1/2013 and 9/11/2020. The inclusion criteria were patients over the age of 16, with a diaphyseal tibial fracture and who underwent a CT of the affected lower limb. The articular fracture extension was categorised into either posterior malleolar (PM) or other fracture.
Results:
764 diaphyseal tibial fractures were analysed, of these 300 had a CT and could be included. There were 127 intra- articular fractures. Of these, 83 (65.4%) cases were PM and 44 were other fractures. On univariate analysis, the PM fractures were associated with fibular spiral (p=-016) fractures and no fracture of the fibular (p=.003), lateral direction of the tibial fracture (p=.04), female gender (p=.002), AO classification 42B1 (p=.033) and an increasing angle of tibial fracture. However, on multivariate regression analysis the only significant factor was a high angle of tibia fracture. Other distal tibia fracture extensions were associated with no fracture of the fibular (p=.002), medial direction of tibia fracture (p=.004), female gender (p=.000), and AO classification 42A1 (p=.004), 42A2 (p=.029), 42B3 (p=035) and 42C2 (p=.032). On multivariate analysis. the lateral direction of tibia fracture, and AO classification 42A1 and 42A2 were significant factors.
Conclusion:
Distal tibia articular extension occurs in almost half of tibial shaft fractures. A number of factors were associated with the extension, however multivariate analysis did not create a suitable prediction model. Nevertheless, rotational tibia fractures with a high angle of fracture should have a low threshold of further investigation with a CT prior to surgical intervention.
A chemometric approach for the quantitative structural analysis of binary blends of copolymers was conducted. Three types of copolymers were synthesized by radical emulsion copolymerization of two ...out of three monomers—acrylonitrile, styrene, and α-methylstyrene—to prepare three series of binary blends of these copolymers. Partial least-squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) regression were conducted with datasets in which the 1H nuclear magnetic resonance (NMR) spectral matrix of the binary blends (explanatory variables) is combined with the blending parameter matrix (objective variables) of the binary blends. The blending parameters, such as chemical compositions and mole fractions of the component copolymers, were successfully predicted without any assignments of the 1H NMR signals through stepwise optimization of the objective and explanatory variables. LASSO regression exhibited higher accuracy than PLS regression, suggesting that the variable selection in LASSO regression was responsible for the improvement in the quantitative prediction.
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•Chemometric quantitative analysis was conducted for binary blends of copolymers.•Blending parameters were predicted by multivariate analysis of 1H NMR spectra.•Predictions of composition and blending fractions of component copolymers were achieved.•Assignments of 1H NMR signals were unnecessary to conduct the predictions.•Quantitative accuracy was improved through stepwise optimization of variables.
Featured Cover Burlot, Jacques; Vangu, Divine; Bellot‐Gurlet, Ludovic ...
Journal of Raman spectroscopy,
02/2024, Letnik:
55, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The cover image is based on the Special Issue – Research Article Raman identification of pigments and opacifiers: Interest and limitation of multivariate analysis by comparison with solid state ...spectroscopical approach—I. Lead‐tin and Naples Yellow by Jacques Burlot et al., https://doi.org/10.1002/jrs.6600.
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will ...occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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