Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling

We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.Comment: Accepted to International Journal of Biostatistic

Early Adoption of Patient Portals by U.S. Hospitals

Customer-facing information systems have received very little research attention, especially in the context of healthcare. Ashospitals begin to provide healthcare consumers with online patient portals to view and manage personal health records anddiagnostic results, little is known about whether or not the ‘dominant paradigm’ (Fichman 2004) of diffusion of innovationstheory is sufficient for explaining the characteristics of early adopters. We suggest that a more nuanced understanding ofearly adoption of patient portals is needed because early adopters are not only the largest hospitals with substantial resourcesand capabilities residing within competitive environments. Specifically, we suggest that patient-portals are impacted bymarket characteristics and require Electronic Medical Records (EMRs) systems to be adopted first. We develop a non-linear,two-stage, econometric model with sample selection correction that controls for EMR adoption and estimates the impact ofdiffusion of innovation and market characteristics on the early adoption of patient portals by U.S. hospitals

Hospital Analytics Adoption: Determinants of Choice and Performance Impacts

Technology investment in the healthcare industry has targeted both transaction support systems, such as Electronic MedicalRecords (EMR), and decision support technologies, such as clinical data warehouses and data mining software. While EMRtechnology adoption has received considerable attention in research studies, decision support technology adoptiondeterminants have received less attention. This study aims to investigate the determinants of adoption of decision analyticssystems in hospitals and the resulting impact on hospital performance. Using the Heckman selection model (to correct fordiscrete strategic decision-making endogeneity) on a cross-sectional and representative set of U.S. hospitals integrated fromvarious data sources, we examine the determinants of choice and resulting quality performance impacts of adopting clinicalanalytics systems. We find that EMR systems implementations are good predictors of clinical analytics systems adoption. Wealso find that the performance impacts of process enabled EMR systems are partially influenced by adoption of analyticssoftware

Cross-National Evidence on Generic Pharmaceuticals: Pharmacy vs. Physician-Driven Markets

This paper examines the role of regulation and competition in generic markets. Generics offer large potential savings to payers and consumers of pharmaceuticals. Whether the potential savings are realized depends on the extent of generic entry and uptake and the level of generic prices. In the U.S., the regulatory, legal and incentive structures encourage prompt entry, aggressive price competition and patient switching to generics. Key features are that pharmacists are authorized and incentivized to switch patients to cheap generics. By contrast, in many other high and middle income countries, generics traditionally competed on brand rather than price because physicians rather than pharmacies are the decision-makers. Physician-driven generic markets tend to have higher generic prices and may have lower generic uptake, depending on regulations and incentives. Using IMS data to analyze generic markets in the U.S., Canada, France, Germany, U.K., Italy, Spain, Japan, Australia, Mexico, Chile, Brazil over the period 1998-2009, we estimate a three-equation model for number of generic entrants, generic prices and generic volume shares. We find little effect of originator defense strategies, significant differences between unbranded and unbranded generics, variation across countries in volume response to prices. Policy changes adopted to stimulate generic uptake and reduce generic prices have been successful in some E.U. countries.

Transgenic mouse model for the formation of Hirano bodies

<p>Abstract</p> <p>Background</p> <p>Hirano bodies are actin-rich cytoplasmic inclusions found predominantly in the brain in association with a variety of conditions including aging and Alzheimer's disease. The function of Hirano bodies in normal aging and in progression of disease has not been extensively investigated due to a lack of experimental model systems. We have developed a transgenic mouse model by expression of a gain-of-function actin cross-linking protein mutant.</p> <p>Results</p> <p>We used the Cre/loxP system to permit tissue specific expression of Hirano bodies, and employed the murine Thy 1 promoter to drive expression of Cre recombinase in the brain. Hirano bodies were observed in the cerebral cortex and hippocampus of homozygous double transgenic 6 month old mice containing Cre. The Hirano bodies were eosinophilic rods, and also exhibited the paracrystalline F-actin filament organization that is characteristic of these inclusions. Mice with Hirano bodies appear healthy and fertile, but exhibited some alterations in both short-term and long-term synaptic plasticity, including paired-pulse depression rather than facilitation, and decreased magnitude of early LTP.</p> <p>Conclusions</p> <p>Hirano bodies are not lethal and appear to have little or no effect on histology and tissue organization. Hirano bodies do modulate synaptic plasticity and exert clearly discernable effects on LTP and paired-pulse paradigms. This model system will allow us to investigate the impact of Hirano bodies <it>in vivo</it>, the pathways for formation and degradation of Hirano bodies, and whether Hirano bodies promote or modulate development of pathology and disease progression.</p

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics

Measuring the performance of prediction models to personalize treatment choice.

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect

Magnetism and Structural Distortion in the La0.7Sr0.3MnO3 Metallic Ferromagnet

Neutron scattering studies on a single crystal of the highly-correlated electron system, La1-xSrxMnO3 with x~0.3, have been carried out elucidating both the spin and lattice dynamics of this metallic ferromagnet. We report a large measured value of the spin wave stiffness constant, which directly shows that the electron transfer energy of the d band is large. The spin dynamics, including magnetic critical scattering, demonstrate that this material behaves similar to other typical metallic ferromagnets such as Fe or Ni. The crystal structure is rhombohedral, as previously reported, for all temperatures studied (below ~425K). We have observed new superlattice peaks which show that the primary rhombohedral lattice distortion arises from oxygen octahedra rotations resulting in an R-3c structure. The superlattice reflection intensities which are very sensitive to structural changes are independent of temperature demonstrating that there is no primary lattice distortion anomaly at the magnetic transition temperature, Tc = 378.1 K, however there is a lattice contraction.Comment: Submitted to Phys. Rev. B. (03Aug95) Uuencoded gz-compressed .tar file of Postscript text (12 pages) and 6 figures. Also available by WWW from http://insti.physics.sunysb.edu/~mmartin/ under my list of publications or by e-mail reques