Regularized Hypothesis Testing in Random Fields with Applications to Neuroimaging / Pruebas de Hipótesis Regularizadas en Campos Aleatorios con Aplicaciones a Neuroimágenes

Abstract The task of determining for which elements of a random field (e.g., pixels in an image) a certain null hypothesis may be rejected is a relevant problem in several scientific areas. In the current contribution, we introduce a new method for performing this task, the regularized hypothesis testing (RHT) method, focusing on its use in neuroimaging research. RHT is based on the formulation of the hypothesis testing task as a Bayesian estimation problem, with the previous application of a Markovian random field. The latter allows for the incorporation of local spatial information and considers different noise models, including spatially correlated noise. In tests on synthetic data showing regular activation levels on uncorrelated noise fields, RHT furnished a true positive rate (TPR) of 0.97, overcoming the state-of-the-art morphology-based hypothesis testing (MBHT) method and the traditional family-wise error rate (FWER) method, which afforded 0.93 and 0.58, respectively. For fields with highly correlated noise, the TPR provided by RHT was 0.65, and by MBHT and FWER was 0.35 and 0.29, respectively. For tests utilizing real functional magnetic resonance imaging (fMRI) data, RHT managed to locate the activation regions when 60% of the original signal were removed, while MBHT located only one region and FWER located none.

Resumen En varias áreas científicas aparece el problema de determinar los elementos de un campo aleatorio (por ejemplo, píxeles en una imagen) en los que se puede rechazar una cierta hipótesis nula. En este artículo presentamos un nuevo método para realizar esta tarea, centrado en aplicaciones para investigación de neuroimagen. Nuestra propuesta se basa en la formulación de pruebas de hipótesis como un problema de estimación Bayesiana, usando como a priori un campo aleatorio Markoviano, que permite incorporar información espacial local y considera diferentes modelos de ruido, incluido el ruido correlacionado espacialmente. Para pruebas en datos sintéticos con niveles de activación regulares sobre campos de ruido no correlacionado, nuestro método obtiene una tasa de verdaderos positivos (TPR) de 0.97, superando al método del estado del arte MBHT y al método de control FWER que obtienen 0.93 y 0.58 respectivamente; para campos con ruido altamente correlacionado, nuestro método obtiene un TPR de 0.65, mientras que MBHT y FWER obtienen 0.35 y 0.29 respectivamente. Para pruebas con datos reales de fMRI, nuestro método localiza las regiones de activación cuando removemos 60% de la señal original, mientras que MBHT no localiza región alguna y FWER localiza una de las dos regiones.

Development of decision system for individualization of vancomycin dosage / 药学学报

Vancomycin has been widely prescribed as the first-line antibiotic in the treatment of methicillin-resistant Staphylococcus aureus and other serious Gram-positive infections. Due to its large pharmacokinetic (PK) variability and narrow therapeutic range, it requires optimization of dosage to achieve target exposure. In this study, SmartDose, a decision support system for individualization of vancomycin dosage is developed using the maximum a posterior Bayesian estimation (MAPB) by the open-source language R combined with the population PK characteristics of vancomycin in Chinese patients. It provides initial design and adjustment of dose regimens based on the therapeutic drug monitoring (TDM) results, as well as a user-defined module to facilitate optimal vancomycin therapy. SmartDose has a high computational reliability, which is validated by NONMEM, the golden standard PK software. Meanwhile, SmartDose is established as a web-based application and its operational flexibility makes it an efficient tool for vancomycin dose optimization in routine clinical settings.

Development of software for individualized dosage regimen of valproic acid and carbamazepine / 中国药学杂志

OBJECTIVE: To develop software for individualized dosage regimen of valproic acid and carbamazepine according to population pharmacokinetics and Bayesian estimation method. METHODS: Based on the prior population pharmacokinetic (PPK) information, Microsoft Excel 2010 was employed as the input-output interface to run the PPK program, Nonlinear Mixed Effect Modeling (NONMEM), to estimate the PK parameters and design the regimen. RESULTS: The software fulfills the functions of patient information management, initial dosage design, dosage calculation by maximum a posterior Bayesian estimation (MAPB) and compliance assessment. CONCLUSION: The established software provides a powerful tool in the clinical settings to facilitate the individualized therapy for the epilepsy patients.

Estudio de la performance del modelo Covello-Merkhofer en la medición del riesgo en planes de vacunación antiaftosa / Performance study of the Covello-Merkhofer model in the treatment of risk in anti-footprint vaccination plans

La cíclica y masiva movilización de terneros puede determinar la recurrencia y persistencia de fiebre aftosa. Por ello, en Sudamérica se promueven esquemas de vacunación en función de la dinámica ganadera. Para evaluar mejor estos esquemas se desarrolló y aplicó un modelo estadístico de cuantificación de riesgos para asistir en la evaluación de planes de vacunación sistemática contra la fiebre aftosa de bovinos. El modelo utiliza información sobre la epidemiología de la fiebre aftosa; de la dinámica poblacional y de vacunación recolectada en un período llamado Ventana de Observación/Estimación y la proyecta hacia una fecha futura ubicada en un período llamado Ventana de Predicción en donde se obtuvo un predictor de la probabilidad de contacto entre terneros no vacunados y bovinos residualmente infectados por el virus de la fiebre aftosa. El modelo utiliza estimación de parámetros obtenidos de la opinión experta; de evidencias empíricas; o de la conjugación de ambos mediante estimadores bayesianos de las distribuciones Beta y Dirichlet. El modelo se aplicó a datos de Argentina mediante simulación de Monte Carlo, permitiendo identificar diferencias significativas al 5% entre los efectos de tres alternativas de vacunación comparadas mediante el método de Bonferroni.(AU)

The recurrence and persistence of fmd could be the consequence of cyclic and massive transportation of calves. For this reason, vaccination schemes related to livestock dynamic are promoted in South America. To improve the evaluation of vaccination schemes a quantitative stochastic risk assessment model was developed and applied in order to aid in the evaluation of strategies of systematic vaccination of cattle against fmd. The model uses information about fmd epidemiology and about population and vaccination dynamics. The information is collected during a period of time called Observation/Estimation Window and projected to a later time called Prediction Window where a predictor of the probability of contact between non-vaccinated calves with residually fmd infected cattle is obtained. Estimates of the parameters of the model are obtained from: expert opinion; empirical evidence or the conjugation of both by means of Bayesian estimators of the Beta and Dirichlet distributions. Applied to data of Argentina, through Monte Carlo simulation, the model allowed the identification of significant statistical differences among the effects of three different vaccination alternatives compared by Bonferroni test.(AU)