Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control

© 2016 Torres, Smith, Mistry, Brincker and Whyatt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail.Peer reviewedFinal Published versio

MODELING OF QUALITY PROFILE DATA WITH APPLICATION IN MANUFACTURING AND BIOMEDICAL ENGINEERING

The quality of the output of a complex system is often recorded as multidimensional profile data with panel structure. In such structure, the quality of each individual in the output is measured repeatedly based on time or other variables. In this dissertation, the quality profile data are modeled to address two types of problems: (a) to explore the underlying relationship between the parameter of interest in the complex system and the resulting quality under the condition that the principal mechanism is not fully known and (b) to quantify the uncertainties among the output. For the first type of problem, we consider a constrained semiparametric varying coefficient model. The system parameter of interest is treated as a covariate whose effect upon the resulting quality is modeled nonparametrically as a function of time. Any existing physicochemical knowledge related to other factors in the system that affect the resulting output quality is modeled parametrically as an additive term in the model. In the situation that expert knowledge about the effect of the parameter is available, some constraints can be incorporated in the model such that the estimated effect aligns with the given knowledge. For the second type of problem, mixed-effect model is developed to quantify the uncertainties among output using random effects. These random effects can be utilized for anomaly detection or for variation quantification where deviation among individuals is of interest depending on the context of the data. Three case studies from manufacturing and biomedical engineering domains are presented in the dissertation where the above two types of problems are discussed

Clinical and molecular correlates in fragile X premutation females.

The prevalence of the fragile X premutation (55-200 CGG repeats) among the general population is relatively high, but there remains a lack of clear understanding of the links between molecular biomarkers and clinical outcomes. In this study we investigated the correlations between molecular measures (CGG repeat size, FMR1 mRNA, FMRP expression levels, and methylation status at the promoter region and in FREE2 site) and clinical phenotypes (anxiety, obsessive compulsive symptoms, depression and executive function deficits) in 36 adult premutation female carriers and compared to 24 normal control subjects. Premutation carriers reported higher levels of obsessive compulsive symptoms, depression, and anxiety, but demonstrated no significant deficits in global cognitive functions or executive function compared to the control group. Increased age in carriers was significantly associated with increased anxiety levels. As expected, FMR1 mRNA expression was significantly correlated with CGG repeat number. However, no significant correlations were observed between molecular (including epigenetic) measures and clinical phenotypes in this sample. Our study, albeit limited by the sample size, establishes the complexity of the mechanisms that link the FMR1 locus to the clinical phenotypes commonly observed in female carriers suggesting that other factors, including environment or additional genetic changes, may have an impact on the clinical phenotypes. However, it continues to emphasize the need for assessment and treatment of psychiatric problems in female premutation carriers

A semi-parametric mixed models for longitudinally measured fasting blood sugar level of adult diabetic patients

Abstract Background At the diabetic clinic of Jimma University Specialized Hospital, health professionals provide regular follow-up to help people with diabetes live long and relatively healthy lives. Based on patient condition, they also provide interventions in the form of counselling to promote a healthy diet and physical activity and prescribing medicines. The main purpose of this study is to estimate the rate of change of fasting blood sugar (FBS) profile experienced by patients over time. The change may help to assess the effectiveness of interventions taken by the clinic to regulate FBS level, where rates of change close to zero over time may indicate the interventions are good regulating the level. Methods In the analysis of longitudinal data, the mean profile is often estimated by parametric linear mixed effects model. However, the individual and mean profile plots of FBS level for diabetic patients are nonlinear and imposing parametric models may be too restrictive and yield unsatisfactory results. We propose a semi-parametric mixed model, in particular using spline smoothing to efficiently analyze a longitudinal measured fasting blood sugar level of adult diabetic patients accounting for correlation between observations through random effects. Results The semi-parametric mixed models had better fit than the linear mixed models for various variance structures of subject-specific random effects. The study revealed that the rate of change in FBS level in diabetic patients, due to the clinic interventions, does not continue as a steady pace but changes with time and weight of patients. Conclusions The proposed method can help a physician in clinical monitoring of diabetic patients and to assess the effect of intervention packages, such as healthy diet, physical activity and prescribed medicines, because individualized curve may be obtained to follow patient-specific FBS level trends

Monitoring regression models for lifetimes

Abstract. Monitoring regression models for lifetimes The current study addresses the monitoring of regression models with response variable having a distribution for lifetimes. Certain aspects of this research have relevant importance. First of all, in most of the existing literature, monitoring regression models is treated as a special case of profile monitoring. However, especially in some industrial and healthcare applications, regression models can adequately represent process quality but cannot always be qualified as profiles. This is the case of regression models for lifetimes. The fact is that lifetimes can be measured just once at most in the same experimental unit. Consequently, the nature of responses while monitoring regression models is not multivariate necessarily. However, the main goal of monitoring regression models for lifetimes aims to check the stability of the distributions of n response variables Yi , i = 1, · · · , n. As all these distributions are linked by the same parameter vector, the stability of the formers depends on the one of the latter. Thus, it is clear that profile monitoring and regression monitoring share the same purpose. Techniques from profile monitoring can be used for successfully monitoring regression models for lifetimes as well. Some methodologies for monitoring Weibull regression models for lifetimes with common shape parameter and in phase II processes will be addressed depending on the composition of available regression data structures. The monitoring of the parameter vector characterizing the Weibull regression model allows us to make conclusions about the mean value of the response variable. It will be shown that the monitoring of regression models for lifetimes can be carried out by redesigning existing methods from monitoring continuous quality variables and profile monitoring. In the presence of uncensored lifetimes, it was found out that it is possible to adapt conventional control charts for single observations to the monitoring of the common shape parameter. It is also possible to adapt control techniques and methodologies from profile monitoring to the case of monitoring the entire parameter vector characterizing the basic model. In both cases, chart designing depends on the asymptotic normality of the maximum likelihood estimator of the parameter vector. Thus, it is necessary to implement some existing corrections to the monitoring statistics so that existing control charts work acceptably well when non-large enough data sets are available. When a type I right-censored mechanism is operating on lifetimes, the monitoring can be carried out with the help of one-sided likelihood ratio based cumulative sum control charts. Theese procedures can be used for monitoring one or more of the parameters in the parameter vector and has practically no restrictions respect to the dataset dimension needed for monitoring. Conducted simulations suggest that this chart is more effective than the multivariate exponentially weighted moving average method when detecting the deterioration of the process is wanted.Monitoreo de modelos de regresión para tiempos de vida El presente estudio se aborda el monitoreo de modelos de regresión para tiempos de vida. Ciertos aspectos de este trabajo son de crucial importancia. Como primera medida, en gran parte de la literatura especializada, el monitoreo de modelos de regresión se trata como un caso particular del monitoreo de perfiles. Sin embargo, existen muchas aplicaciones, especialmente en ingeniería y en cuidados en salud, en las cuales los modelos de regresión pueden caracterizar adecuadamente la calidad de los procesos pero no siempre pueden considerarse como perfiles. Es el caso de los modelos de regresión para tiempos de vida. El hecho es que, en general, un tiempo de vida puede medirse a lo sumo una vez en la misma unidad experimental. Consecuentemente, la naturaleza de las respuestas en el monitoreo de modelos de regresión no necesariamente es multivariada. Sin embargo, el objetivo principal del montireo de modelos regresión apunta a verificar la estabilidad de las distribuciones n variables respuesta Yi , i = 1, · · · , n. Como todas estas distribuciones están relacionadas entre sí por un único vector de parámetros, la estabilidad de las primeras depende de la estabilidad de este último. De este modo, es claro que tanto el monitoreo de modelos de regresión como el de perfiles comparten el mismo propósito. Es así como las técnicas usadas para monitorear perfiles pueden también usarse par monitorear acertadamente los modelos de regresión para tiempos de vida. Se presentan algunas metodologías para monitorear modelos de regresión para tiempos de vida con respuesta Weibull, dependiendo de cómo están conformadas los conjuntos de datos disponibles. El monitoreo del vector de parámetros de modelos de regresión Weibull permite hacer conclusiones acerca del valor medio de la variable respuesta. Se mostrará además que se puede encarar el monitoreo de modelos de regresión para tiempos de vida mediante el rediseño de las metodologías de control que comúnmente se usan para monitorear variables de calidad continuas o para monitorear perfiles. Cuando la respuesta no es censurada, se encontr´o que es posible adaptar las cartas de control convencionales para observaciones individuales de la característica de calidad, al monitoreo del parámtero de forma de un modelo de regresión Weibull. Es posible también adaptar las metodologías de control usadas en el monitoreo de perfiles para monitorear todo el vector de parámetros que caracterizan los modelos de regresión Weibull. En ambos casos, el diseño de las cartas se basa en la normalidad asintótica del estimador máximo verosímil del vector de parámetros. Por consiguiente, se hace necesario implementar correcciones existentes a las estadísticas de monitoreo para que las cartas de control trabajen aceptablemente aún cuando no se disponga de conjuntos de datos lo suficientemente grandes. Cuando un mecanismo de censura a derecha de tipo I opera sobre los tiempos de vida, se puede realizar el monitoreo con la ayuda de cartas de control unilaterales de sumas acumuladas basadas en la estadística de razón de verosimilitudes. Estos esquemas se pueden utilizar para monitorear uno o varios parámetros que conforman el vector de parámetros y prácticamente no tienen restricciones respecto a la cantidad de observaciones necesarias para realizar el monitoreo. Los estudios de simulación sugieren que estos esquemas son más efectivos que los métodos multivariados de promedios móviles ponderados exponencialmente cuando se desea detectar el deterioro de los procesos de calidad.Doctorad

Integrated Projection and Regression Models for Monitoring Multivariate Autocorrelated Cascade Processes

This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed. The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed