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A Bayesian model and design are described for a phase I–II trial to optimize jointly the doses of a targeted agent and a chemotherapy agent for solid tumours. A challenge in designing the trial was that both the efficacy and the toxicity outcomes were defined as four‐level ordinal variables. To reflect possibly complex joint effects of the two doses on each of the two outcomes, for each marginal distribution...
Mortality forecasts are typically limited in that they pertain only to national death rates, predict only all‐cause mortality or do not capture and utilize the correlation between diseases. We present a novel Bayesian hierarchical model that jointly forecasts cause‐specific death rates for geographic subunits. We examine its effectiveness by applying it to US vital statistics data for 1979–2011 and...
We consider the problem of designing additional experiments to update statistical models for latent day specific effects. The problem appears in thermal spraying, where particles are sprayed on surfaces to obtain a coating. The relationships between in‐flight properties of the particles and the controllable variables are modelled by generalized linear models. However, there are also non‐controllable...
New computer algorithms for finding D‐optimal designs of stimulus sequence for functional magnetic resonance imaging (MRI) experiments are proposed. Although functional MRI data are commonly analysed by linear models, the construction of a functional MRI design matrix is much more complicated than in conventional experimental design problems. Inspired by the widely used exchange algorithm technique,...
Design conditions for marine structures are typically informed by threshold‐based extreme value analyses of oceanographic variables, in which excesses of a high threshold are modelled by a generalized Pareto distribution. Too low a threshold leads to bias from model misspecification, and raising the threshold increases the variance of estimators: a bias–variance trade‐off. Many existing threshold...
Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical post‐processing. In the ensemble model output statistics approach, a probabilistic forecast is given by a...
Statistical models used to estimate the spatiotemporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterized by a spatially smooth evolution between some pairs of adjacent areal units...
We propose novel methods for predictive (sparse) principal component analysis with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring that the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements...
Group sequential study designs have been proposed as an approach to conserve resources in biomarker validation studies. Typically, group sequential study designs allow both ‘early termination to reject the null hypothesis’ and ‘early termination for futility’ if there is evidence against the alternative hypothesis. In contrast, several researchers have advocated for using group sequential study designs...
Estimation of the effect of a treatment in the presence of unmeasured confounding is a common objective in observational studies. The two‐stage least squares instrumental variables procedure is frequently used but is not applicable to time‐to‐event data if some observations are censored. We develop a simultaneous equations model to account for unmeasured confounding of the effect of treatment on survival...
We propose a hidden Markov mixture model for the analysis of gene expression measurements mapped to chromosome locations. These expression values represent preprocessed light intensities observed in each probe of Affymetrix oligonucleotide arrays. Here, the algorithm BLAT is used to align thousands of probe sequences to each chromosome. The main goal is to identify genome regions associated with...
In large multilevel studies effects of interest are often evaluated for a number of more or less related outcomes. For instance, the present work was motivated by the multiplicity issues that arose in the analysis of a cluster‐randomized, crossover intervention study evaluating the health benefits of a school meal programme. We propose a novel and versatile framework for simultaneous inference on...
We propose a three‐state frailty Markov model coupled with likelihood‐based inference to analyse tooth level life course data in caries research. This analysis is challenging because of intraoral clustering, interval censoring, multiplicity of caries states and computational complexities. We also develop a Bayesian approach to predict future caries transition probabilities given observed life history...
In population size estimation, many capture–recapture‐type data exhibit a preponderance of ‘1’‐counts. This excess of 1s can arise as subjects gain information from the initial capture that provides a desire and ability to avoid subsequent captures. Existing population size estimators that purport to deal with heterogeneity can be much too large in the presence of 1‐inflation, which is a specific...
The increasing awareness of treatment effect heterogeneity has motivated flexible designs of confirmatory clinical trials that prospectively allow investigators to test for treatment efficacy for a subpopulation of patients in addition to the entire population. If a target subpopulation is not well characterized in the design stage, it can be developed at the end of a broad eligibility trial under...
Symbolic or categorical sequences occur in many contexts and can be characterized, for example, by integer‐valued intersymbol distances or binary‐valued indicator sequences. The analysis of these numerical sequences often sheds light on the properties of the original symbolic sequences. This work introduces new statistical tools for exploring auto‐correlation structure in the indicator sequences,...
We introduce a non‐stationary spatiotemporal model for gridded data on the sphere. The model specifies a computationally convenient covariance structure that depends on heterogeneous geography. Widely used statistical models on a spherical domain are non‐stationary for different latitudes, but stationary at the same latitude (axial symmetry). This assumption has been acknowledged to be too restrictive...
Consumer products and services can often be described as mixtures of ingredients. Examples are the mixture of ingredients in a cocktail and the mixture of different components of travel time (e.g. in‐vehicle and out‐of‐vehicle travel time) in a transportation setting. Choice experiments may help to determine how the respondent's choice of a product or service is affected by the combination of ingredients...
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