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Summary. The dose finding problem for single compounds has been generalized to combination therapies and several binary regression models have been proposed in the literature. We propose general tools to evaluate them, in particular dose‐free parameterizations of the risks of toxicity and risk ratio functions and plots. New classes of risk functions are also proposed. They generate low dimensional...
Summary. The paper addresses the optimal choice of the waiting period (or time out) that a device should respect before entering sleep mode, to optimize a trade‐off between power consumption and device availability. The optimal time out is inferred by appropriate statistical modelling of the times between user requests. In our first approach, these times are assumed to be independent, and a constant...
Summary. Motivated by a practical application, the paper investigates robust estimation of economic indicators from survey samples based on a semiparametric Pareto tail model. Economic performance is typically measured by a set of indicators, which are often estimated from survey data—the motivating example being the European indicators on social exclusion and poverty computed from the well‐known...
Summary. The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is relatively small and/or certain outcome categories are rare, maximum likelihood can yield biased estimates of the regression parameters. Firth and Kosmidis proposed a procedure to remove the leading...
Summary. The paper is devoted to the development of a statistical framework for air quality assessment at the country level and for the evaluation of the ambient population exposure and risk with respect to airborne pollutants. The framework is based on a multivariate space–time model and on aggregated indices defined at different levels of aggregation in space and time. The indices are evaluated,...
Summary. Auxiliary variables that are associated with both key survey variables and response propensity are important for post‐survey non‐response adjustments, but rare. Interviewer observations on sample units and linked auxiliary variables from commercially available household databases are promising candidates, but these variables are prone to error. The assumption of missingness at random that...
Summary. Linear multiregression dynamic models, which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending...
Summary. To prevent accidents caused by unexploded bombs from the Second World War, high‐risk zones need to be determined. We introduce two statistical methods to determine such zones by considering patterns of exploded and unexploded bombs as realizations of inhomogeneous spatial Poisson point processes. The first method is based on the intensity of the point process; the second method on its nearest...
Summary. The paper describes a complex multistate modelling approach for the analysis of association between the onset of diabetes and the diagnosis of pancreatic cancer among families with hereditary pancreatitis. The model allows for competing risks, correlated survival times and time‐dependent covariates, so taking several interrelated factors into account: the consequences of pancreatic resection,...
Summary. The paper describes a Bayesian spatial discrete time survival model to estimate the effect of air pollution on the risk of preterm birth. The standard approach treats prematurity as a binary outcome and cannot effectively examine time varying exposures during pregnancy. Time varying exposures can arise either in short‐term lagged exposures due to seasonality in air pollution or long‐term...
Summary. Data with mixed‐type (metric–ordinal–nominal) variables are typical for social stratification, i.e. partitioning a population into social classes. Approaches to cluster such data are compared, namely a latent class mixture model assuming local independence and dissimilarity‐based methods such as k‐medoids. The design of an appropriate dissimilarity measure and the estimation of the number...
Summary. In a unique longitudinal study of teen driving, risky driving behaviour and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and developing a predictor of crashes from previous risky driving behaviour. In this work, we propose two latent class models for relating risky driving...
Summary. Proteins are the workhorses of all living systems, and protein bioinformatics deals with analysis of protein sequences (one dimensional) and structures (three dimensional). The paper reviews statistical advances in three major active areas of protein structural bioinformatics: structure comparison, Ramachandran plots and structure prediction. These topics play a key role in understanding...
Summary. We consider the problem of transforming values from a flow time series observed over varying time intervals into values that cover calendar intervals such as day, week, month, quarter and year. We call this process calendarization. We propose simple methods based on interpolating the cumulated flows with natural spline interpolations. Alternatively, we provide state space models with missing...
Summary. Cardiovascular ischaemic diseases are one of the main causes of death all over the world. In this class of pathologies, a quick diagnosis is essential for a good prognosis in reperfusive treatment. In particular, an automatic classification procedure based on statistical analysis of teletransmitted electrocardiograph (‘ECG’) traces would be very helpful for an early diagnosis. This work...
Ecological momentary assessment is a method for collecting realtime data in subjects' environments. It often uses electronic devices to obtain information on psychological state through administration of questionnaires at times that are selected from a probability‐based sampling design. This information can be used to model the effect of momentary variation in psychological state on the lifetimes...
Summary. Hierarchical models (HMs) have been used extensively in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for other pollutants and other time varying factors. Recently, the US Environmental Protection Agency has called for research quantifying health effects of simultaneous exposure to many air pollutants. However, straightforward...
Summary. The paper presents a Bayesian approach to analysing interval‐censored data with random unknown end points. Such data occur when the event of interest is interval censored but, because of the measurement process, the interval end points are not known exactly. Modelling the measurement process permits inference that accounts for this source of variability. Our results are motivated by an experimental...
Acute infectious diseases are transmitted over networks of social contacts. Epidemic models are used to predict the spread of emergent pathogens and to compare intervention strategies. Many of these models assume equal probability of contact within mixing groups (homes, schools, etc.), but little work has inferred the actual contact network, which may influence epidemic estimates. We develop a penalized...
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