Structural equation modeling for those who think they don't care

We will discuss SEM (structural equation modeling), not from the perspective of the models for which it is most often used--measurement models, confirmatory factor analysis, and the like--but from the perspective of how it can extend other estimators. From a wide range of choices, we will focus on extensions of mixed models (random and fixed-effects regression). Extensions include conditional effects (not completely random), endogenous covariates, and others.

Ultrahigh-Field MRI in Human Ischemic Stroke – a 7 Tesla Study

INTRODUCTION: Magnetic resonance imaging (MRI) using field strengths up to 3 Tesla (T) has proven to be a powerful tool for stroke diagnosis. Recently, ultrahigh-field (UHF) MRI at 7 T has shown relevant diagnostic benefits in imaging of neurological diseases, but its value for stroke imaging has not been investigated yet. We present the first evaluation of a clinically feasible stroke imaging protocol at 7 T. For comparison an established stroke imaging protocol was applied at 3 T. METHODS: In a prospective imaging study seven patients with subacute and chronic stroke were included. Imaging at 3 T was immediately followed by 7 T imaging. Both protocols included T1-weighted 3D Magnetization-Prepared Rapid-Acquired Gradient-Echo (3D-MPRAGE), T2-weighted 2D Fluid Attenuated Inversion Recovery (2D-FLAIR), T2-weighted 2D Fluid Attenuated Inversion Recovery (2D-T2-TSE), T2* weighted 2D Fast Low Angle Shot Gradient Echo (2D-HemoFLASH) and 3D Time-of-Flight angiography (3D-TOF). RESULTS: The diagnostic information relevant for clinical stroke imaging obtained at 3 T was equally available at 7 T. Higher spatial resolution at 7 T revealed more anatomical details precisely depicting ischemic lesions and periinfarct alterations. A clear benefit in anatomical resolution was also demonstrated for vessel imaging at 7 T. RF power deposition constraints induced scan time prolongation and reduced brain coverage for 2D-FLAIR, 2D-T2-TSE and 3D-TOF at 7 T versus 3 T. CONCLUSIONS: The potential of 7 T MRI for human stroke imaging is shown. Our pilot study encourages a further evaluation of the diagnostic benefit of stroke imaging at 7 T in a larger study

DTOBIT2: Stata module to estimate a tobit model with marginal effects at observed censoring rate

Estimates a tobit model and provides a table of marginal effects evaluated at the observed censoring rate of the dependent variable. The marginal effects are computed for the dependent variable conditional on the censoring and on the unconditional expected value of the dependent variable.

Stata graphics, under the hood

Stata's graphics are more flexible than many realise. We will exploit this flexibility and explore a potpourri of topics, some of interest to all graphers and others primarily of interest to those creating highly customised graphs or new graph commands. Among the topics will be creating custom schemes to control the appearance of graphs, including an overview of the format and contents of scheme files. We will also examine twoway graphs as a platform for creating custom graphs, some of which are not readily apparent. We will discuss techniques for managing data and leveraging twoway's native plottypes. Along the way we will introduce some new official and unofficial tools, and perhaps some downright dangerous, but useful, undocumented tricks.

HECKMAN2: Stata module to estimate Heckman selection model using Heckman's two-step approach

heckman2 performs both Heckman's two-step procedure with Heckman's adjusted covariance matrix of the estimates for estimation of selection models.

GRAND2: Stata module to compute an estimate of the grand mean/intercept and differences

For use after fit to present a set of indicator/dummy variables in the form of a "grand mean" and differences from the "grand mean". Windows users should not attempt to download these files with a web browser. The specified list of variables (indicator_variable_list) must be orthogonal and completely span the space. The model estimated by fit must also include the complete list of indicator variables that fully span the space. Use the hascons option on fit to allow the full set of indicators to be included in the model. grand2 cannot be used after regress, it requires some of the results stored by fit. grand2 recasts the full parameter and variance estimates from the prior fit and saves the result as the current estimates. In this sense, grand2 acts like an estimator. The coefficients, standard errors, and matrices may be accessed in the same way as any estimator; specifically, test may be used to perform tests with the grand mean and differences.

GRAND: Stata modules to compute grand mean and dummies for differences

For use after fit to present a set of indicator/dummy variables in a "grand mean" and difference from the "grand mean" form. The specified list of variables (indicator_variable_list) must be orthogonal and completely span the space. The model estimated by fit must include the complete list of indicator variables that fully span space. Use the hascons or nocons on fit to allow the full set of indicators to be included in the model. This is version 1.01 of this software.