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In mobility network capacity planning, characterizing the mobility network traffic is one of the most challenging tasks. Besides the growth trend and multiple periodic temporal patterns for normal traffic, the problem is complicated by the occasionally intense traffic for special events and its dynamic spatial relationships. Identifying the areas that have different traffic patterns compared with...
Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome this challenge, we employ a basis representation of the model outputs and observations: we match these decompositions to carry out the calibration efficiently...
Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and...
Ranking sportsmen whose careers took place in different eras is often a contentious issue and the topic of much debate. We focus on cricket and examine what conclusions may be drawn about the ranking of test batsmen by using data on batting scores from the first test in 1877 onwards. The overlapping nature of playing careers is exploited to form a bridge from past to present so that all players can...
Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on a health outcome of interest such as mortality and morbidity. Most previous DLM approaches consider only one pollutant at a time. We propose a distributed lag interaction model to characterize the joint lagged effect of two pollutants. One natural way to model the interaction...
The rise of ‘big data’ has led to the frequent need to process and store data sets containing large numbers of high dimensional observations. Because of storage restrictions, these observations might be recorded in a lossy‐but‐sparse manner, with information collapsed onto a few entries which are considered important. This results in informative missingness in the observed data. Our motivating application...
The integrated nested Laplace approximation (INLA) is a convenient way to obtain approximations to the posterior marginals for parameters in Bayesian hierarchical models when the latent effects can be expressed as a Gaussian Markov random field. In addition, its implementation in the R‐INLA package for the R statistical software provides an easy way to fit models using the INLA in practice in a fraction...
Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta‐analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field...
Preferential sampling in geostatistics occurs when the locations at which observations are made may depend on the spatial process that underlines the correlation structure of the measurements. We show that previously proposed Monte Carlo estimates for the likelihood function may not be approximating the desired function. Furthermore, we argue that, for preferential sampling of moderate complexity,...
We present a two‐stage phase I–II design of a combination of two drugs in cancer clinical trials. The goal is to estimate safe dose combination regions with a desired level of efficacy. In stage I, conditional escalation with overdose control is used to allocate dose combinations to successive cohorts of patients and the maximum tolerated dose curve is estimated as a function of Bayes estimates...
We consider a situation where rich historical data are available for the coefficients and their standard errors in an established regression model describing the association between a binary outcome variable Y and a set of predicting factors X, from a large study. We would like to utilize this summary information for improving estimation and prediction in an expanded model of interest, Y|X,B. The...
We are interested in the temporal trends of biomarkers that are related to disease progression, especially the association between two temporal trends. When biological mechanisms are lacking, no parametric forms of the temporal trends are theoretically justified. In this work, we adopt joint non‐parametric local linear mixed effects modelling. By local linear regression, each temporal trend is represented...
In cross‐breeding experiments it can be of interest to see whether there are any synergistic effects of certain genes. This could be by being particularly useful or detrimental to the individual. This type of effect involving multiple genes is called epistasis. Epistatic interactions can affect growth, fertility traits or even cause complete lethality. However, detecting epistasis in genomewide studies...
Immunotherapy has been hailed as the biggest breakthrough for treating cancer since the first development of chemotherapy. The new features of immunotherapy make the traditional clinical trial paradigm increasingly inefficient and dysfunctional. We propose a Bayesian phase I–II design for immunotherapy trials called BDFIT to find the optimal biological dose (OBD). We jointly model the toxicity outcome,...
Recommended phase 2 doses for some drugs may differ according to a patient's clinical or genetic characteristics. We develop a new method that determines the individualized optimal dose according to patterns of patient covariates and selects the covariates that are associated with efficacy and toxicity in early phase trials for evaluating multiple patient covariates of interest. To address the difficulty...
Clinical trials in vulnerable populations are extremely difficult to conduct. A sequential phase I–II trial aimed at finding the appropriate dose of levetiracetam for treating neonatal seizures was planned with a maximum sample size of 50 newborns. Three primary outcomes are considered: efficacy and two types of toxicity that occur at the same time but are measured at different time points. In the...
The landscape of oncology drug development has recently changed with the emergence of molecularly targeted agents and immunotherapies. These new therapeutic agents appear more likely to induce multiple low or moderate grade toxicities rather than dose limiting toxicities. Various model‐based dose finding designs and toxicity severity scoring systems have been proposed to account for toxicity grades,...
We propose a flexible design for the identification of optimal dose combinations in dual‐agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual‐agent dose finding and are supported by the numerical results...
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