The convenient calculation of some test statistics in models of discrete choice

The paper considers the use of artificial regression in calculating different types of score test when the log

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

Computing Markov-Perfect Optimal Policies in Business-Cycle Models

Time-inconsistency is an essential feature of many policy problems (Kydland and Prescott, 1977). This paper presents and compares three methods for computing Markov-perfect optimal policies in stochastic nonlinear business cycle models. The methods considered include value function iteration, generalized Euler-equations, and parameterized shadow prices. In the context of a business cycle model in which a scal authority chooses government spending and income taxation optimally, while lacking the ability to commit, we show that the solutions obtained using value function iteration and generalized Euler equations are somewhat more accurate than that obtained using parameterized shadow prices. Among these three methods, we show that value function iteration can be applied easily, even to environments that include a risk-sensitive scal authority and/or inequality constraints on government spending. We show that the risk-sensitive scal authority lowers government spending and income-taxation, reducing the disincentive households face to accumulate wealth.

Validation and long-term evaluation of a modified on-line chiral analytical method for therapeutic drug monitoring of (R,S)-methadone in clinical samples.

Matrix effects, which represent an important issue in liquid chromatography coupled to mass spectrometry or tandem mass spectrometry detection, should be closely assessed during method development. In the case of quantitative analysis, the use of stable isotope-labelled internal standard with physico-chemical properties and ionization behaviour similar to the analyte is recommended. In this paper, an example of the choice of a co-eluting deuterated internal standard to compensate for short-term and long-term matrix effect in the case of chiral (R,S)-methadone plasma quantification is reported. The method was fully validated over a concentration range of 5-800 ng/mL for each methadone enantiomer with satisfactory relative bias (-1.0 to 1.0%), repeatability (0.9-4.9%) and intermediate precision (1.4-12.0%). From the results obtained during validation, a control chart process during 52 series of routine analysis was established using both intermediate precision standard deviation and FDA acceptance criteria. The results of routine quality control samples were generally included in the +/-15% variability around the target value and mainly in the two standard deviation interval illustrating the long-term stability of the method. The intermediate precision variability estimated in method validation was found to be coherent with the routine use of the method. During this period, 257 trough concentration and 54 peak concentration plasma samples of patients undergoing (R,S)-methadone treatment were successfully analysed for routine therapeutic drug monitoring.

Structured and unstructured continuous models for Wolbachia infections

We introduce and investigate a series of models for an infection of a diplodiploid host species by the bacterial endosymbiont Wolbachia. The continuous models are characterized by partial vertical transmission, cytoplasmic incompatibility and fitness costs associated with the infection. A particular aspect of interest is competitions between mutually incompatible strains. We further introduce an age-structured model that takes into account different fertility and mortality rates at different stages of the life cycle of the individuals. With only a few parameters, the ordinary differential equation models exhibit already interesting dynamics and can be used to predict criteria under which a strain of bacteria is able to invade a population. Interestingly, but not surprisingly, the age-structured model shows significant differences concerning the existence and stability of equilibrium solutions compared to the unstructured model.

Prospective validation of the Pulmonary Embolism Severity Index. A clinical prognostic model for pulmonary embolism

Practice guidelines recommend outpatient care for selected patients with non-massive pulmonary embolism (PE), but fail to specify how these low-risk patients should be identified. Using data from U.S. patients, we previously derived the Pulmonary Embolism Severity Index (PESI), a prediction rule that risk stratifies patients with PE. We sought to validate the PESI in a European patient cohort. We prospectively validated the PESI in patients with PE diagnosed at six emergency departments in three European countries. We used baseline data for the rule's 11 prognostic variables to stratify patients into five risk classes (I-V) of increasing probability of mortality. The outcome was overall mortality at 90 days after presentation. To assess the accuracy of the PESI to predict mortality, we estimated the sensitivity, specificity, and predictive values for low- (risk classes I/II) versus higher-risk patients (risk classes III-V), and the discriminatory power using the area under the receiver operating characteristic (ROC) curve. Among 357 patients with PE, overall mortality was 5.9%, ranging from 0% in class I to 17.9% in class V. The 186 (52%) low-risk patients had an overall mortality of 1.1% (95% confidence interval [CI]: 0.1-3.8%) compared to 11.1% (95% CI: 6.8-16.8%) in the 171 (48%) higher-risk patients. The PESI had a high sensitivity (91%, 95% CI: 71-97%) and a negative predictive value (99%, 95% CI: 96-100%) for predicting mortality. The area under the ROC curve was 0.78 (95% CI: 0.70-0.86). The PESI reliably identifies patients with PE who are at low risk of death and who are potential candidates for outpatient care. The PESI may help physicians make more rational decisions about hospitalization for patients with PE.

PSMA-Specific CAR-Engineered T Cells Eradicate Disseminated Prostate Cancer in Preclinical Models.

Immunology-based interventions have been proposed as a promising curative chance to effectively attack postoperative minimal residual disease and distant metastatic localizations of prostate tumors. We developed a chimeric antigen receptor (CAR) construct targeting the human prostate-specific membrane antigen (hPSMA), based on a novel and high affinity specific mAb. As a transfer method, we employed last-generation lentiviral vectors (LV) carrying a synthetic bidirectional promoter capable of robust and coordinated expression of the CAR molecule, and a bioluminescent reporter gene to allow the tracking of transgenic T cells after in vivo adoptive transfer. Overall, we demonstrated that CAR-expressing LV efficiently transduced short-term activated PBMC, which in turn were readily stimulated to produce cytokines and to exert a relevant cytotoxic activity by engagement with PSMA+ prostate tumor cells. Upon in vivo transfer in tumor-bearing mice, CAR-transduced T cells were capable to completely eradicate a disseminated neoplasia in the majority of treated animals, thus supporting the translation of such approach in the clinical setting.

The role of fixed delays in neuronal rate models

Fixed delays in neuronal interactions arise through synaptic and dendritic processing. Previous work has shown that such delays, which play an important role in shaping the dynamics of networks of large numbers of spiking neurons with continuous synaptic kinetics, can be taken into account with a rate model through the addition of an explicit, fixed delay. Here we extend this work to account for arbitrary symmetric patterns of synaptic connectivity and generic nonlinear transfer functions. Specifically, we conduct a weakly nonlinear analysis of the dynamical states arising via primary instabilities of the stationary uniform state. In this way we determine analytically how the nature and stability of these states depend on the choice of transfer function and connectivity. While this dependence is, in general, nontrivial, we make use of the smallness of the ratio in the delay in neuronal interactions to the effective time constant of integration to arrive at two general observations of physiological relevance. These are: 1 - fast oscillations are always supercritical for realistic transfer functions. 2 - Traveling waves are preferred over standing waves given plausible patterns of local connectivity.

Validation of the German version of the Career Adapt-Abilities Scale and its relation to orientations to happiness and work stress

Career adapt-ability has recently gained momentum as a psychosocial construct that not only has much to offer the field of career development, but also contributes to positive coping, adjustment and self-regulation through the four dimensions of concern, control, curiosity and confidence. The positive psychology movement, with concepts such as the orientations to happiness, explores the factors that contribute to human flourishing and optimum functioning. This research has two main contributions; 1) to validate a German version of the Career Adapt-Abilities Scale (CAAS), and 2) to extend the contribution of adapt-abilities to the field of work stress and explore its mediating capacity in the relation between orientations to happiness and work stress. We used a representative sample of the German-speaking Swiss working population including 1204 participants (49.8% women), aged between 26 and 56 (Mage = 42.04). Results indicated that the German version of the CAAS is valid, with overall high levels of model fit suggesting that the conceptual structure of career adapt-ability replicates well in this cultural context. Adapt-abilities showed a negative relationship to work stress, and a positive one with orientations to happiness. The engagement and pleasure scales of orientations to happiness also correlated negatively with work stress. Moreover, career adapt-ability mediates the relationship between orientations to happiness and work stress. In depth analysis of the mediating effect revealed that control is the only significant mediator. Thus control may be acting as a mechanism through which individuals attain their desired life at work subsequently contributing to reduced stress levels.

Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradient is subject to the interplay of biotic interactions in complement to abiotic environmental filtering. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose to use food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant-herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve both species distribution and community forecasts. Most importantly, this combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may be recurrent. Our combined approach points a promising direction forward to model the spatial variation of entire species interaction networks. Our work has implications for studies of range shifting species and invasive species biology where it may be unknown how a given biota might interact with a potential invader or in future climate.