Víctor Casero-Alonso

Un siglo separa la prueba de las tazas de té, nacida de un reto entre amigos en la estación experimental de Rothamsted, de los experimentos modernos. Lo que comenzó como una apuesta informal acabaría transformando la manera de hacer ciencia. El desafío, sin embargo, permanece intacto: distinguir los efectos reales de las simples fluctuaciones del azar.

It is common in the optimal design literature to assume that the model has no random effects and it is known, apart from model parameters. Random effects models are widely applied across all disciplines, particularly in the life sciences and clinical studies. This paper assumes there are several plausible models and we want to find a design that optimally discriminates among models with random effects in them. A common design criterion for discriminating between models with fixed effects is the Kullback-Leibler (KL) divergence criterion. It is a maximin-type of criterion, not differentiable and is a 3-layer nested optimization problem over very distinct domains. Consequently, optimal designs for discriminating models are notoriously difficult to determine, not only analytically, but also computationally. This paper provides theoretical results for the KL-optimality criterion value for discriminating among random effects models and implements a nature-inspired metaheuristic algorithm to facilitate the search for an optimal discrimination design. Our methodology is quite general and applies to discriminating random effects models with multiple interacting experimental conditions which may be continuous or discrete. We provide two applications; the first finds a design that optimally discriminates among fractional polynomials with a single continuous variable, and the second identifies the best design to discriminate among several multi-factor random effects models.

The combustion of two non-woody types of biomass (almond shells and exhausted olive cake) in a domestic boiler at different loads was studied in order to evaluate their suitability as fuels. To select the optimal boiler operating conditions (excess air, primary/secondary air ratio and grate vibration), which allows for lower CO and particulate matter emissions for each biomass and load, a statistical design of experiments was performed. Similar optimal operating conditions were found for both fuels at nominal load (excess air: 1.5, primary/secondary air ratio: 20/80), the grate vibration being the only parameter to be modified due to the different ash content (45 and 20 s for almond shells and exhausted olive cake, respectively). At partial load, a slightly higher excess air (1.6) and a higher proportion of primary air (50/50) were needed in the case of almond shells. Results showed higher CO and lower NO_x and PM emissions at partial load for both fuels. The high ash content of exhausted olive cake deteriorated its combustion process (accumulated ashes were observed in the fireplace). Gaseous and solid emissions did not fulfil the UNE-EN 303-5 limits for any fuel or condition; although, almond shells seem to be a much more suitable fuel since they could be used just blended with a small quantity of a high-quality biomass or additive. However, exhausted olive cake not only led to a very poor efficiency at partial load (74%), clearly below the minimum required by the standard (77%), but also to an unacceptable pollutant emission level. So, this latter fuel would require a high blending ratio with another type of biomass, pre-treatments for reducing the alkali compounds and/or significant technological modifications allowing for a proper ash handling.

Fractional polynomials (FP) have been shown to be more flexible than polynomial models for fitting data from an univariate regression model with a continuous outcome but design issues for FP models have lagged. We focus on FPs with a single variable and construct D-optimal designs for estimating model parameters and I-optimal designs for prediction over a user-specified region of the design space. Some analytic results are given, along with a discussion on model uncertainty. In addition, we provide an applet to facilitate users find tailor made optimal designs for their problems. As applications, we construct optimal designs for three studies that used FPs to model risk assessments of (a) testosterone levels from magnesium accumulation in certain areas of the brains in songbirds, (b) rats subject to exposure of different chemicals, and (c) hormetic effects due to small toxic exposure. In each case, we elaborate the benefits of having an optimal design in terms of cost and quality of the statistical inference.

In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s prior experience. The main goal of this paper is to study the effect, in terms of efficiency, when misspecification in the probability distribution of the response variable occurs. The elemental information matrix, which includes information on the probability distribution of the response variable, provides a generalized Fisher information matrix. This study is performed from a practical perspective, comparing a normal distribution with the Poisson or gamma distribution. First, analytical results are obtained, including results for the linear quadratic model, and these are applied to some real illustrative examples. The nonlinear 4-parameter Hill model is next considered to study the influence of misspecification in a dose-response model. This analysis shows the behavior of the efficiency of the designs obtained in the presence of misspecification, by assuming heteroscedastic normal distributions with respect to the D-optimal designs for the gamma, or Poisson, distribution, as the true one.

The aim of this retrospective and international study is to identify those clinical variables associated with diffuse alveolar damage (DAD), and to explore the impact of DAD on hospital mortality risk. Inclusion criteria were: adult patients with acute respiratory distress syndrome (ARDS) undergoing open lung biopsy (OLB) during their intensive care unit (ICU) management. The main end-points were: DAD and hospital mortality. In the training (n = 193) and validation cohorts (n = 65), the respiratory rate (odd ratio (OR) 0.956; confidence interval (CI) 95% 0.918; 0.995) and coronary ischemia (OR 5.974; CI95% 1.668; 21.399) on the day of ARDS had an average area under the receiver operating characteristic curve (AUROC) of 0.660 (CI95% 0.585; 0.736) and 0.562 (0.417; 0.706), respectively. PEEP (OR 1.131; CI95% 1.051; 1.218) and coronary ischemia (OR 6.820; CI95% 1.856; 25.061) on the day of OLB had an average AUROC of 0.696 (CI95% 0.621; 0.769) and 0.534 (CI95% 0.391; 0.678), respectively, to predict DAD. DAD (OR 2.296; CI95% 1.228; 4.294), diabetes mellitus requiring insulin (OR 0.081; CI95% 0.009; 0.710) and the respiratory rate (OR 1.045; CI95% 1.001; 1.091) on the day of ARDS had an average AUROC of 0.659 (CI95% 0.583; 0.737) and 0.513 (CI95% 0.361; 0.664) to predict hospital mortality and DAD (OR 2.081; CI95% 1.053; 4.114), diabetes mellitus requiring insulin (OR 0.093; CI95% 0.009; 0.956), PaCO₂ (OR 1.051; CI95% 1.019; 1.084), and platelets count (OR 0.999; CI95% 0.999; 0.999) the day of OLB had an average AUROC of 0.778 (CI95% 0.710; 0.843) and 0.634 (CI95%0.481; 0.787) to predict hospital mortalty in the training and validation cohorts, respectively. In conclusion, DAD could not to be predicted clinically and was significantly associated with hospital mortality.

Orthorexia nervosa (ON) is characterized by an obsession with healthy eating, which may lead to severe physical, psychological and social disorders. It is particularly important to research this problem in populations that do not receive clinical care in order to improve early detection and treatment. The aim of this study was to research the prevalence of ON in a population of Spanish university students and to analyze the possible associations between ON and psychological traits and behaviors that are common to ED. A cross-sectional study with 454 students from the University of Castilla La Mancha, Spain. In total, 295 women and 159 men participated, aged between 18 and 41 years. The ORTO-11-ES questionnaire and the Eating Disorder Inventory (EDI-2) were used for this study. The chi squared test was used to compare the homogeneity among the different groups. The scores on the ORTO-11-ES suggested that 17% of students were at risk of ON. The scores on the EDI-2 for the group at risk of ON were significant, compared to the remaining individuals, regarding their drive for thinness (17.1% vs 2.1%), bulimia (2.6% vs 0%), body dissatisfaction (26.3% vs. 12.4%), perfectionism (14.5% vs 4.8%), interoceptive awareness (13.2% vs 1.3%), asceticism (15.8% vs 3.7%) and impulsiveness (9.2% vs 1.9%). These findings suggest that many of the psychological and behavioral aspects of ED are shared by people who are at risk of ON. Future research should use longitudinal data, examining the temporal relationship among these variables or other underlying variables that may contribute to the concurrence of ED and ON.