Reduction in the use of diagnostic tests in infants with risk factors for early-onset neonatal sepsis does not delay antibiotic treatment.

Despite a low positive predictive value, diagnostic tests such as complete blood count (CBC) and C-reactive protein (CRP) are commonly used to evaluate whether infants with risk factors for early-onset neonatal sepsis (EOS) should be treated with antibiotics. We investigated the impact of implementing a protocol aiming at reducing the number of diagnostic tests in infants with risk factors for EOS in order to compare the diagnostic performance of repeated clinical examination with CBC and CRP measurement. The primary outcome was the time between birth and the first dose of antibiotics in infants treated for suspected EOS. Among the 11,503 infants born at ≥35 weeks during the study period, 222 were treated with antibiotics for suspected EOS. The proportion of infants receiving antibiotics for suspected EOS was 2.1% and 1.7% before and after the change of protocol (p = 0.09). Reduction of diagnostic tests was associated with earlier antibiotic treatment in infants treated for suspected EOS (hazard ratio 1.58; 95% confidence interval [CI] 1.20-2.07; p <0.001), and in infants with neonatal infection (hazard ratio 2.20; 95% CI 1.19-4.06; p = 0.01). There was no difference in the duration of hospital stay nor in the proportion of infants requiring respiratory or cardiovascular support before and after the change of protocol. Reduction of diagnostic tests such as CBC and CRP does not delay initiation of antibiotic treatment in infants with suspected EOS. The importance of clinical examination in infants with risk factors for EOS should be emphasised.

Patients' understanding of blood tests and attitudes to HIV screening in the emergency department of a Swiss teaching hospital: a cross-sectional observational study.

BACKGROUND: In Switzerland, patients may undergo "blood tests" without being informed what these are screening for. Inadequate doctor-patient communication may result in patient misunderstanding. We examined what patients in the emergency department (ED) believed they had been screened for and explored their attitudes to routine (non-targeted) human immunodeficiency virus (HIV) screening. METHODS: Between 1st October 2012 and 28th February 2013, a questionnaire-based survey was conducted among patients aged 16-70 years old presenting to the ED of Lausanne University Hospital. Patients were asked: (1) if they believed they had been screened for HIV; (2) if they agreed in principle to routine HIV screening and (3) if they agreed to be HIV tested during their current ED visit. RESULTS: Of 466 eligible patients, 411 (88%) agreed to participate. Mean age was 46 ± 16 years; 192 patients (47%) were women; 366 (89%) were Swiss or European; 113 (27%) believed they had been screened for HIV, the proportion increasing with age (p ≤0.01), 297 (72%) agreed in principle with routine HIV testing in the ED, and 138 patients (34%) agreed to be HIV tested during their current ED visit. CONCLUSION: In this ED population, 27% believed incorrectly they had been screened for HIV. Over 70% agreed in principle with routine HIV testing and 34% agreed to be tested during their current visit. These results demonstrate willingness among patients concerning routine HIV testing in the ED and highlight a need for improved doctor-patient communication about what a blood test specifically screens for.

The Reluctance of Cattle to Change a Learned Choice May Confound Preference Tests

The conclusion of the article states "it appears that previously learned choices may affect future choices in Y-mazes for cattle. Another area that needs to be researched is the effects of a mildly aversive treatment versus a severely aversive treatment on the tendency of a bovine to resist changing a learned choice".

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the latter statistics are pivotal under the null, which provides the justification for applying MC tests. Furthermore, we extend the exact independence test proposed by Harvey and Phillips (1982) to the multi-equation framework. Specifically, we introduce several induced tests based on a set of simultaneous Harvey/Phillips-type tests and suggest a simulation-based solution to the associated combination problem. The properties of the proposed tests are studied in a Monte Carlo experiment which shows that standard asymptotic tests exhibit important size distortions, while MC tests achieve complete size control and display good power. Moreover, MC-QLR tests performed best in terms of power, a result of interest from the point of view of simulation-based tests. The power of the MC induced tests improves appreciably in comparison to standard Bonferroni tests and, in certain cases, outperforms the likelihood-based MC tests. The tests are applied to data used by Fischer (1993) to analyze the macroeconomic determinants of growth.

Économétrie, théorie des tests et philosophie des sciences

Dans ce texte, nous revoyons certains développements récents de l’économétrie qui peuvent être intéressants pour des chercheurs dans des domaines autres que l’économie et nous soulignons l’éclairage particulier que l’économétrie peut jeter sur certains thèmes généraux de méthodologie et de philosophie des sciences, tels la falsifiabilité comme critère du caractère scientifique d’une théorie (Popper), la sous-détermination des théories par les données (Quine) et l’instrumentalisme. En particulier, nous soulignons le contraste entre deux styles de modélisation - l’approche parcimonieuse et l’approche statistico-descriptive - et nous discutons les liens entre la théorie des tests statistiques et la philosophie des sciences.

Logique et tests d'hypotheses: reflexions sur les problemes mal poses en econometrie

Dans ce texte, nous analysons les développements récents de l’économétrie à la lumière de la théorie des tests statistiques. Nous revoyons d’abord quelques principes fondamentaux de philosophie des sciences et de théorie statistique, en mettant l’accent sur la parcimonie et la falsifiabilité comme critères d’évaluation des modèles, sur le rôle de la théorie des tests comme formalisation du principe de falsification de modèles probabilistes, ainsi que sur la justification logique des notions de base de la théorie des tests (tel le niveau d’un test). Nous montrons ensuite que certaines des méthodes statistiques et économétriques les plus utilisées sont fondamentalement inappropriées pour les problèmes et modèles considérés, tandis que de nombreuses hypothèses, pour lesquelles des procédures de test sont communément proposées, ne sont en fait pas du tout testables. De telles situations conduisent à des problèmes statistiques mal posés. Nous analysons quelques cas particuliers de tels problèmes : (1) la construction d’intervalles de confiance dans le cadre de modèles structurels qui posent des problèmes d’identification; (2) la construction de tests pour des hypothèses non paramétriques, incluant la construction de procédures robustes à l’hétéroscédasticité, à la non-normalité ou à la spécification dynamique. Nous indiquons que ces difficultés proviennent souvent de l’ambition d’affaiblir les conditions de régularité nécessaires à toute analyse statistique ainsi que d’une utilisation inappropriée de résultats de théorie distributionnelle asymptotique. Enfin, nous soulignons l’importance de formuler des hypothèses et modèles testables, et de proposer des techniques économétriques dont les propriétés sont démontrables dans les échantillons finis.

Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions.

In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments. We suggest controlling the size of such tests (under nonparametric assumptions) by using permutational versions of the tests jointly with the method of Monte Carlo tests properly adjusted to deal with discrete distributions. We also propose a combined test procedure, whose level is again perfectly controlled through the Monte Carlo test technique and has better power properties than the individual tests that are combined. Finally, in a simulation experiment, we show that the technique suggested provides perfect control of test size and that the new tests proposed can yield sizeable power improvements.

An Eigenfunction Approach for Volatility Modeling.

In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential or affine), we assume that it is a linear combination of the eigenfunctions of the conditional expectation (resp. infinitesimal generator) operator associated to the state variable in discrete (resp. continuous) time. Special examples are the popular log-normal and square-root models where the eigenfunctions are the Hermite and Laguerre polynomials respectively. The eigenfunction approach has at least six advantages: i) it is general since any square integrable function may be written as a linear combination of the eigenfunctions; ii) the orthogonality of the eigenfunctions leads to the traditional interpretations of the linear principal components analysis; iii) the implied dynamics of the variance and squared return processes are ARMA and, hence, simple for forecasting and inference purposes; (iv) more importantly, this generates fat tails for the variance and returns processes; v) in contrast to popular models, the variance of the variance is a flexible function of the variance; vi) these models are closed under temporal aggregation.