Optimized Computer-Generated Motions for Animation

Computer programmers working on computer animation have long been trying to solve the problem of how to move objects in user-desired ways with a minimum of user interaction. Objects moving from one place to another move along a path often determined by a spline. We would like to be able to allow the user a specify a characteristic of the object's motion and the animation system to choose a motion path that evidences that characteristic. We develop an approach using constrained optimization that will create paths. Some interesting motions have been found. We describe the effects obtainable from this method so that an animator can sensibly choose between them. We found that minimization of the covariant acceleration of all the points in a body leads to motion that is attractive. This motion seems to cause the moving body to anticipate its motion path in order to prevent sudden moves. It also seems to create very fluid-appearing motions because it tries to avoid sharp turns and sudden stops

Integrated Delivery Networks: In Search of Benefits and Market Effects

Integrated Delivery Networks (IDNs) have very different stated purposes than mere collections of hospitals: to coordinate care across the continuum of health services and to manage population health. IDN advocates claim that these complex enterprises yield both societal benefits and performance advantages over less integrated competitors. The purpose of this analysis is to evaluate the evidence to support these claims.For the study, researchers performed a review of the academic literature on IDN performance, as well as an analysis of publicly available quality and financial data from 15 of the biggest not-for-profit IDNs in the U.S., including Sutter Health in Northern California. The authors compared the publicly available performance information on the IDNs' flagship hospital in its principal regional market with that flagship's most significant in-market competitor. The study found that it is possible for integrated delivery networks to offer meaningful benefits, but there is little evidence they have reduced costs or improved the quality of care. Findings include:Hospital-physician integration has raised physician costs, hospital prices and per capita medical care spending;Hospital integration into health plan operations and capitated contracting was not associated either with clinical efficiency or financial efficiencyProviders are likely to see a decrease in operating margins and return on capital as they invest in IDN developmen

Longitudinal Scalar-on-Function Regression with Application to Tractography Data

We propose a class of estimation techniques for scalar-on-function regression in longitudinal studies where both outcomes, such as test results on motor functions, and functional predictors, such as brain images, may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging (DTI) tractography study. One of the primary goals of the study is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and functional predictors measurements per patient; (3) Gaussian or non-Gaussian outcomes; and, (4) missing values within functional predictors. We review existing approaches designed to handle such types of data and discuss their limitations. We propose two versions of a new method, longitudinal functional principal components regression. These methods extend the well-known functional principal component regression and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The different methods are compared in simulation studies, and the most promising approaches are used for analyzing the tractography data

Nonlinear tube-fitting for the analysis of anatomical and functional structures

We are concerned with the estimation of the exterior surface and interior summaries of tube-shaped anatomical structures. This interest is motivated by two distinct scientific goals, one dealing with the distribution of HIV microbicide in the colon and the other with measuring degradation in white-matter tracts in the brain. Our problem is posed as the estimation of the support of a distribution in three dimensions from a sample from that distribution, possibly measured with error. We propose a novel tube-fitting algorithm to construct such estimators. Further, we conduct a simulation study to aid in the choice of a key parameter of the algorithm, and we test our algorithm with validation study tailored to the motivating data sets. Finally, we apply the tube-fitting algorithm to a colon image produced by single photon emission computed tomography (SPECT) and to a white-matter tract image produced using diffusion tensor imaging (DTI).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS384 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The existence of so many uninsured Americans is driving the dynamics pushing hospitals and physicians into an adversarial position

ABSTRACT: Hospital-physician relationships in the United States have deteriorated mark

CORRECTED CONFIDENCE BANDS FOR FUNCTIONAL DATA USING PRINCIPAL COMPONENTS

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this paper, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- based and decomposition-based variability are constructed. Standard mixed-model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Iterated expectation and variance formulas combine both sources of uncertainty by combining model-based conditional estimates across the distribution of decompositions. Our method compares favorably to competing approaches in simulation studies that include both densely- and sparsely-observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented as the IVfpca() function in the R package refund on CRAN

RESTRICTED LIKELIHOOD RATIO TESTS FOR FUNCTIONAL EFFECTS IN THE FUNCTIONAL LINEAR MODEL

The goal of our article is to provide a transparent, robust, and computationally feasible statistical approach for testing in the context of scalar-on-function linear regression models. In particular, we are interested in testing for the necessity of functional effects against standard linear models. Our methods are motivated by and applied to a large longitudinal study involving diffusion tensor imaging of intracranial white matter tracts in a susceptible cohort. In the context of this study, we conduct hypothesis tests that are motivated by anatomical knowledge and which support recent findings regarding the relationship between cognitive impairment and white matter demyelination. R-code and data are provided to reproduce the application

LONGITUDINAL PENALIZED FUNCTIONAL REGRESSION

We propose a new regression model and inferential tools for the case when both the outcome and the functional exposures are observed at multiple visits. This data structure is new but increasingly present in applications where functions or images are recorded at multiple times. This raises new inferential challenges that cannot be addressed with current methods and software. Our proposed model generalizes the Generalized Linear Mixed Effects Model (GLMM) by adding functional predictors. Smoothness of the functional coefficients is ensured using roughness penalties estimated by Restricted Maximum Likelihood (REML) in a corresponding mixed effects model. This method is computationally feasible and is applicable when the functional predictors are measured densely, sparsely or with error; code implementing the proposed procedure is freely available. Methods are applied to a longitudinal diffusion tensor imaging (DTI) study relating changes in the microstructure of intracranial white matter tracts to cognitive disability in multiple sclerosis patients, but we note that the discussed data structure is increasingly common and our methods apply generally. An online appendix compares two implementations, one likelihood-based and the other Bayesian, and provides the software used in simulations

PENALIZED FUNCTIONAL REGRESSION

We develop fast fitting methods for generalized functional linear models. An undersmooth of the functional predictor is obtained by projecting on a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. Our approach can be implemented using standard mixed effects software and is computationally fast. Our methodology is motivated by a diffusion tensor imaging (DTI) study. The aim of this study is to analyze differences between various cerebral white matter tract property measurements of multiple sclerosis (MS) patients and controls. While the statistical developments proposed here were motivated by the DTI study, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. An online appendix provides R implementations of all simulations