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Summary. Estimating the burden of infectious disease is complicated by the general tendency for underreporting of cases. When the reporting rate is unknown, conventional methods have relied on accounting methods that do not make explicit use of surveillance data or the temporal dynamics of transmission and infection. State space models are a framework for various methods that allow dynamic models...
Summary. The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5–6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work...
Summary. Healthcare resource allocation decisions are commonly informed by computer model predictions of population mean costs and health effects. It is common to quantify the uncertainty in the prediction due to uncertain model inputs, but methods for quantifying uncertainty due to inadequacies in model structure are less well developed. We introduce an example of a model that aims to predict the...
Summary. Clinical data on the location of residence at the time of diagnosis of new lupus cases in Toronto, Canada, for the 40 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Inference is complicated by numerous irregular changes in the census regions on which population is reported. A model is introduced consisting of a continuous random spatial surface and fixed...
Summary. Numerous studies have linked ambient air pollution and adverse health outcomes. Many studies of this nature relate outdoor pollution levels measured at a few monitoring stations with health outcomes. Recently, computational methods have been developed to model the distribution of personal exposures, rather than ambient concentration, and then relate the exposure distribution to the health...
Summary. In oncology, progression‐free survival time, which is defined as the minimum of the times to disease progression or death, often is used to characterize treatment and covariate effects. We are motivated by the desire to estimate the progression time distribution on the basis of data from 780 paediatric patients with choroid plexus tumours, which are a rare brain cancer where disease progression...
Summary. The paper will help practitioners to select strength 3 designs that are useful for screening both main effects and two‐factor interactions. We calculated word length patterns, correlations of four‐factor interaction contrast vectors with the intercept and ranks of the two‐factor interaction matrices for all non‐equivalent two‐level orthogonal arrays of strength 3 and run sizes up to 48....
Summary. Multiphase (stage) designs that involve more than two phases are increasingly used by clinicians and psychologists for diagnosis and screening of dementia and many other diseases, e.g. colorectal or breast cancer. The multiphase design is an extension of the commonly used two‐phase design, where an inexpensive initial screening test is followed by a gold standard. In a typical three‐phase...
Summary. In longitudinal genetic studies, investigators collect repeated measurements on a trait that changes with time along with genetic markers. For family‐based longitudinal studies, since repeated measurements are nested within subjects and subjects are nested within families, both the subject level and the measurement level correlations must be taken into account in the statistical analysis...
Summary. In integrated population modelling in ecology, where data from multiple surveys are analysed simultaneously, the Kalman filter may be used to approximate a component likelihood for a state space model of population count data. We evaluate a new method for initiating this Kalman filter, based on a stable age distribution. The new method is illustrated and compared with alternative approaches...
Summary. The measurement of human immunodeficiency virus ribonucleic acid levels over time leads to censored longitudinal data. Suitable models for dynamic modelling of these levels need to take this data characteristic into account. If groups of patients with different developments of the levels over time are suspected the model class of finite mixtures of mixed effects models with censored data...
Summary. Modern epidemiology often requires testing of the effect of a covariate on multiple end points from the same study. However, popular state of the art methods for multiple testing require the tests to be evaluated within the framework of a single model unifying all end points. This severely limits their use in applications where there are different types of end point, e.g. binary, continuous...
Summary. Data from experiments in steady state enzyme kinetic studies and radioligand binding assays are usually analysed by fitting non‐linear models developed from biochemical theory. Designing experiments for fitting non‐linear models is complicated by the fact that the variances of parameter estimates depend on the unknown values of these parameters and Bayesian optimal exact design for non‐linear...
Summary. An automated approach to extract interpretable features of univariate or multivariate profiles (functional data) is proposed. A landmark alignment algorithm is modified and the alignment is combined with piecewise linear approximations. Least absolute shrinkage and selection operator (lasso) regression is used for selecting the most important intercepts and slopes and yields an alternative...
Summary. We investigate the 20‐year‐average boreal winter temperatures generated by an ensemble of six regional climate models (RCMs) in phase I of the North American Regional Climate Change Assessment Program. We use the long‐run average (20‐year integration) to smooth out variability and to capture the climate properties from the RCM outputs. We find that, although the RCMs capture the large‐scale...
Summary We propose a randomized phase II clinical trial design based on Bayesian adaptive randomization and predictive probability monitoring. Adaptive randomization assigns more patients to a more efficacious treatment arm by comparing the posterior probabilities of efficacy between different arms. We continuously monitor the trial using the predictive probability. The trial is terminated early when...
Summary. The paper is concerned with a dynamic factor model for spatiotemporal coupled environmental variables. The model is proposed in a state space formulation which, through Kalman recursions, allows a unified approach to prediction and estimation. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo algorithms for dynamic linear models...
Summary. Characterizing the quality of dispersion of nanocomposites presents a challenging statistical problem for which no direct method has been fully adopted. A high precision, statistically well‐grounded measure is required which is suitable for dealing with a single small non‐homogeneous particle pattern obtained from the material. Our approach uses the Delaunay network of particles to measure...
Summary. Group (pooled) testing is often used to reduce the total number of tests that are needed to screen a large number of individuals for an infectious disease or some other binary characteristic. Traditionally, research in group testing has assumed that each individual is independent with the same risk of positivity. More recently, there has been a growing set of literature generalizing previous...
Summary. Recommendation systems are emerging as an important business application with significant economic impact. Currently popular systems include Amazon's book recommendations, Netflix's movie recommendations and Pandora's music recommendations. We address the problem of estimating probabilities associated with recommendation system data by using non‐parametric kernel smoothing. In our estimation...
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