Aplicació en línia d'un sistema variable de control multivariable en un Reactor ..

L’objecte del present treball és la realització d’una aplicació que permeti portar a terme el control estadístic multivariable en línia d’una planta SBR.Aquesta eina ha de permetre realitzar un anàlisi estadístic multivariable complet del lot en procés, de l’últim lot finalitzat i de la resta de lots processats a la planta.L’aplicació s’ha de realitzar en l’entorn LabVIEW. L’elecció d’aquest programa vecondicionada per l’actualització del mòdul de monitorització de la planta que s’estàdesenvolupant en aquest mateix entorn

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically.

Stratification of the severity of critically ill patients with classification trees

Background: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). Methods: Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). Results: CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69- 75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). Conclusion: With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.

Estudio de la distribución espacial de Cydia pomonella (L.) y Pandemis heparana (Denis & Schiffermüller) en Torregrossa (Lleida) mediante métodos geoestadísticos

El estudio de la distribución espacial de una especie por me'todos geoestadísticos se realiza mediante el conocimiento de la función semivariograma. Después de calcular el semivariograma se procede a la estimación de la variable regionalizada en cualquier punto de la zona de estudio. Esta estimación se realiza mediante técnicas de interpolación lineal llamadas «krigeado», en honor a Krige y Matheron, fundadores de la geoestadística. El «krigeado» se basa en la minimización de la varianza del error en cada punto de estudio, previamente localizado en el espacio por sus coordenadas de situación. Cydia pomonella (L.) y Pandemis heparana (Denis & Schiffermüller) son dos tortrícidos plaga del manzano y del peral. La estimación de sus poblaciones se realiza mediante trampas de feromona y es posible disponer de una amplia base de datos. El objetivo de este trabajo fue analizar la idoneidad de los métodos geoestadísticos para el estudio de poblaciones de insectos y aplicarlas al caso concreto de C. pomonella y P. heparana. Se utilizaron las capturas en 55 estaciones con trampas de feromonas (difusor de origen Wageningen) colocadas en parcelas comerciales de manzano y peral en 1996 y 1997 en el término municipal de Torregrossa (Lleida). La idoneidad de los métodos geoestadísticos quedó demostrada por el hecho de que la variable número acumulado de machos por trampa fue regionalizable. Una vez calculadas las funciones semivariograma para cada especie y año, se han dibujado los mapas de distribución mediante el uso de isolíneas. En el futuro, se plantea la posibilidad de ampliar la zona de estudio a toda la zona frutera de Lleida y analizar la influencia de variables independientes (climáticas ...) sobre la distribución espacial mediante métodos de «co-krigeado».

Salivary cotinine concentrations in daily smokers in Barcelona, Spain: a cross-sectional study

Background: Characterizing and comparing the determinant of cotinine concentrations in different populations should facilitate a better understanding of smoking patterns and addiction. This study describes and characterizes determinants of salivary cotinine concentration in a sample of Spanish adult daily smoker men and women. Methods: A cross-sectional study was carried out between March 2004 and December 2005 in a representative sample of 1245 people from the general population of Barcelona, Spain. A standard questionnaire was used to gather information on active tobacco smoking and passive exposure, and a saliva specimen was obtained to determine salivary cotinine concentration. Two hundred and eleven adult smokers (>16 years old) with complete data were included in the analysis. Determinants of cotinine concentrations were assessed using linear regression models. Results: Salivary cotinine concentration was associated with the reported number of cigarettes smoked in the previous 24 hours (R2 = 0.339; p < 0.05). The inclusion of a quadratic component for number of cigarettes smoked in the regression analyses resulted in an improvement of the fit (R2 = 0.386; p < 0.05). Cotinine concentration differed significantly by sex, with men having higher levels. Conclusion: This study shows that salivary cotinine concentration is significantly associated with the number of cigarettes smoked and sex, but not with other smoking-related variables.

Problems of the randomization test for AB designs

N = 1 designs imply repeated registrations of the behaviour of the same experimental unit and the measurements obtained are often few due to time limitations, while they are also likely to be sequentially dependent. The analytical techniques needed to enhance statistical and clinical decision making have to deal with these problems. Different procedures for analysing data from single-case AB designs are discussed, presenting their main features and revising the results reported by previous studies. Randomization tests represent one of the statistical methods that seemed to perform well in terms of controlling false alarm rates. In the experimental part of the study a new simulation approach is used to test the performance of randomization tests and the results suggest that the technique is not always robust against the violation of the independence assumption. Moreover, sensitivity proved to be generally unacceptably low for series lengths equal to 30 and 40. Considering the evidence available, there does not seem to be an optimal technique for single-case data analysis

The Total Deviation Index estimated by Tolerance Intervals to evaluate the concordance of measurement devices

Background In an agreement assay, it is of interest to evaluate the degree of agreement between the different methods (devices, instruments or observers) used to measure the same characteristic. We propose in this study a technical simplification for inference about the total deviation index (TDI) estimate to assess agreement between two devices of normally-distributed measurements and describe its utility to evaluate inter- and intra-rater agreement if more than one reading per subject is available for each device. Methods We propose to estimate the TDI by constructing a probability interval of the difference in paired measurements between devices, and thereafter, we derive a tolerance interval (TI) procedure as a natural way to make inferences about probability limit estimates. We also describe how the proposed method can be used to compute bounds of the coverage probability. Results The approach is illustrated in a real case example where the agreement between two instruments, a handle mercury sphygmomanometer device and an OMRON 711 automatic device, is assessed in a sample of 384 subjects where measures of systolic blood pressure were taken twice by each device. A simulation study procedure is implemented to evaluate and compare the accuracy of the approach to two already established methods, showing that the TI approximation produces accurate empirical confidence levels which are reasonably close to the nominal confidence level. Conclusions The method proposed is straightforward since the TDI estimate is derived directly from a probability interval of a normally-distributed variable in its original scale, without further transformations. Thereafter, a natural way of making inferences about this estimate is to derive the appropriate TI. Constructions of TI based on normal populations are implemented in most standard statistical packages, thus making it simpler for any practitioner to implement our proposal to assess agreement.

¿Cómo optimizar una matriz de datos? Utilidades básicas del SPSS

Una vez se dispone de los datos introducidos en el paquete estadístico del SPSS (Statistical Package of Social Science), en una matriz de datos, es el momento de plantearse optimizar esa matriz para poder extraer el máximo rendimiento a los datos, según el tipo de análisis que se pretende realizar. Para ello, el propio SPSS tiene una serie de utilidades que pueden ser de gran utilidad. Estas utilidades básicas pueden diferenciarse según su funcionalidad entre: utilidades para la edición de datos, utilidades para la modificación de variables, y las opciones de ayuda que nos brinda. A continuación se presentan algunas de estas utilidades.

Análisis Discriminante mediante SPSS

El análisis discriminante es un método estadístico a través del cual se busca conocer qué variables, medidas en objetos o individuos, explican mejor la atribución de la diferencia de los grupos a los cuales pertenecen dichos objetos o individuos. Es una técnica que nos permite comprobar hasta qué punto las variables independientes consideradas en la investigación clasifican correctamente a los sujetos u objetos. Se muestran y explican los principales elementos que se relacionan con el procedimiento para llevar a cabo el análisis discriminante y su aplicación utilizando el paquete estadístico SPSS, versión 18, para el desarrollo del modelo estadístico, las condiciones para la aplicación del análisis, la estimación e interpretación de las funciones discriminantes, los métodos de clasificación y la validación de los resultados.

Hardness of FRHC-Cu determined by statistical analysis

A statistical indentation method has been employed to study the hardness value of fire-refined high conductivity copper, using nanoindentation technique. The Joslin and Oliver approach was used with the aim to separate the hardness (H) influence of copper matrix, from that of inclusions and grain boundaries. This approach relies on a large array of imprints (around 400 indentations), performed at 150 nm of indentation depth. A statistical study using a cumulative distribution function fit and Gaussian simulated distributions, exhibits that H for each phase can be extracted when the indentation depth is much lower than the size of the secondary phases. It is found that the thermal treatment produces a hardness increase, due to the partly re-dissolution of the inclusions (mainly Pb and Sn) in the matrix.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Com fer una anàlisi quantitativa de dades de tipus descriptiu amb el paquet estadístic de l"SPSS?

En l"actualitat és difícil parlar de processos estadístics d"anàlisi quantitativa de dades sense fer referència a la informàtica aplicada a la recerca. Aquests recursos informàtics es basen sovint en paquets de programes informàtics que tenen l"objectiu d"ajudar al/a la investigador/a en la fase d"anàlisi de dades. En aquests moments un dels paquets més perfeccionats i complets és l"SPSS (Statistical Package for the Social Sciences). L"SPSS és un paquet de programes per tal de dur a terme l"anàlisi estadística de les dades. Constitueix una aplicació estadística força potent, de la qual s"han anat desenvolupant diverses versions des dels seus inicis, als anys setanta. En aquest manual les sortides d"ordinador que es presenten pertanyen a la versió 11.0.1. No obstant això, tot i que la forma ha anat variant des dels inicis, pel que fa al funcionament segueix essent molt similar entre les diferents versions. Abans d"iniciar-nos en la utilització de les aplicacions de l"SPSS és important familiaritzarse amb algunes de les finestres que més farem servir. En entrar a l"SPSS el primer que ens trobem és l"editor de dades. Aquesta finestra visualitza, bàsicament, les dades que anirem introduint. L"editor de dades inclou dues opcions: la Vista de les dades i la de les variables. Aquestes opcions es poden seleccionar a partir de les dues pestanyes que es presenten en la part inferior. La vista de dades conté el menú general i la matriu de dades. Aquesta matriu s"estructura amb els casos a les files i les variables a les columnes.

Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically.