Comparing predictive abilities of longitudinal child growth models

© 2018 John Wiley & Sons, Ltd. The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well-being of children across the world. The project aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave-one-out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America

Requirements for Defining Utility Drive Cycles: An Exploratory Analysis of Grid Frequency Regulation Data for Establishing Battery Performance Testing Standards

Battery testing procedures are important for understanding battery performance, including degradation over the life of the battery. Standards are important to provide clear rules and uniformity to an industry. The work described in this report addresses the need for standard battery testing procedures that reflect real-world applications of energy storage systems to provide regulation services to grid operators. This work was motivated by the need to develop Vehicle-to-Grid (V2G) testing procedures, or V2G drive cycles. Likewise, the stationary energy storage community is equally interested in standardized testing protocols that reflect real-world grid applications for providing regulation services. As the first of several steps toward standardizing battery testing cycles, this work focused on a statistical analysis of frequency regulation signals from the Pennsylvania-New Jersey-Maryland Interconnect with the goal to identify patterns in the regulation signal that would be representative of the entire signal as a typical regulation data set. Results from an extensive time-series analysis are discussed, and the results are explained from both the statistical and the battery-testing perspectives. The results then are interpreted in the context of defining a small set of V2G drive cycles for standardization, offering some recommendations for the next steps toward standardizing testing protocols

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p

The big five personality traits, perfectionism and their association with mental health among UK students on professional degree programmes

Background In view of heightened rates of suicide and evidence of poor mental health among healthcare occupational groups, such as veterinarians, doctors, pharmacists and dentists, there has been increasing focus on the students aiming for careers in these fields. It is often proposed that a high proportion of these students may possess personality traits which render them vulnerable to mental ill-health. Aim To explore the relationship between the big five personality traits, perfectionism and mental health in UK students undertaking undergraduate degrees in veterinary medicine, medicine, pharmacy, dentistry and law. Methods A total of 1744 students studying veterinary medicine, medicine, dentistry, pharmacy and law in the UK completed an online questionnaire, which collected data on the big five personality traits (NEO-FFI), perfectionism (Frost Multidimensional Perfectionism Scale), wellbeing (Warwick-Edinburgh Mental Well-being Scale), psychological distress (General Health Questionnaire-12), depression (Beck Depression Inventory-II) and suicidal ideation and attempts. Results Veterinary, medical and dentistry students were significantly more agreeable than law students, while veterinary students had the lowest perfectionism scores of the five groups studied. High levels of neuroticism and low conscientiousness were predictive of increased mental ill-health in each of the student populations. Conclusions The study highlights that the prevailing anecdotal view of professional students possessing maladaptive personality traits that negatively impact on their mental health may be misplaced

Local regression models: Advancements, applications, and new methods

Local regression methods model the relationship between an independent and dependent variable through weighted fitting of polynomials in local neighborhoods of the design space. A popular method, loess, is a local regression method with favorable statistical and computational properties. Loess modeling has been adapted to the modeling of time series data with deterministic seasonal and trend components with the STL method (seasonal trend decomposition using loess). The first part of this work deals with some enhancements to the STL method. The second part presents an application of STL to syndromic surveillance data. Many of the improvements to STL were motivated by this application. Finally, a new modeling approach to nonparametric density estimation method, called ed, is presented which uses local regression to obtain density estimates. Enhancements to the STL method presented in this work include support for local quadratic smoothing, missing values, and statistical inference. Also, a new method for improving smoothing at the endpoints is presented. This method, called blending, takes the endpoint fit to be a weighted average between the original endpoint fit and another fit of smaller degree. Guidelines are given for the blending parameters. Software with these enhancements is also described. Syndromic surveillance is the monitoring of public health data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods. Methods for modeling daily chief complaint counts presented in this work are based on STL and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, a new synoptic-scale (days to weeks) outbreak detection method was developed and evaluated by a simulation study, comparing detection performance to four well-known methods. The STL detection method performs very well and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide insight into patterns in the data. The ed method of density estimation for a univariate x takes a model building approach: an estimation method that can accurately fit many density patterns in data, and leads to diagnostic visual displays for checking fits for different values of the tuning parameters. The two-step estimator begins with a small-bandwidth balloon density estimate, which is the inverse of the distance of x to the κ-th nearest neighbor. Next, loess is used to smooth the log of the balloon estimate as a function of x. This turns nonparametric density estimation into nonparametric regression estimation, allowing the full power of regression diagnostics, model selection, statistical inference, and computational methods. The ed method is straightforward. It deals well with problems encountered by other methods such as mis-fitting peaks and valleys, and it allows for simple identification and fitting of features such as discontinuities and boundary cut-offs all within the same framework

A Visual Analytics Approach to Understanding Spatiotemporal Hotspots

As data sources become larger and more complex, the ability to effectively explore and analyze patterns amongst varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contains multiple variables, high signal to noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis eneration/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal datasets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geo-spatiotemporal datasets

Requirements for Defining Utility Drive Cycles: An Exploratory Analysis of Grid Frequency Regulation Data for Establishing Battery Performance Testing Standards

Battery testing procedures are important for understanding battery performance, including degradation over the life of the battery. Standards are important to provide clear rules and uniformity to an industry. The work described in this report addresses the need for standard battery testing procedures that reflect real-world applications of energy storage systems to provide regulation services to grid operators. This work was motivated by the need to develop Vehicle-to-Grid (V2G) testing procedures, or V2G drive cycles. Likewise, the stationary energy storage community is equally interested in standardized testing protocols that reflect real-world grid applications for providing regulation services. As the first of several steps toward standardizing battery testing cycles, this work focused on a statistical analysis of frequency regulation signals from the Pennsylvania-New Jersey-Maryland Interconnect with the goal to identify patterns in the regulation signal that would be representative of the entire signal as a typical regulation data set. Results from an extensive time-series analysis are discussed, and the results are explained from both the statistical and the battery-testing perspectives. The results then are interpreted in the context of defining a small set of V2G drive cycles for standardization, offering some recommendations for the next steps toward standardizing testing protocols

Lumen Maintenance Testing of the Philips 60-Watt Replacement Lamp L Prize Entry

This paper describes testing conducted to evaluate the Philips' L Prize award winning 60-watt LED replacement product's ability to meet the lifetime/lumen maintenance requirement of the competition, which was: "having 70 percent of the lumen value under subparagraph (A) [producing a luminous flux greater than 900 lumens] exceeding 25,000 hours under typical conditions expected in residential use." A custom test apparatus was designed and constructed for this testing and a statistical approach was developed for use in evaluating the test results. This will be the only publicly available, third-party data set of long-term LED product operation