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Journal of the Royal Statistical Society. Series C, Applied Statistics

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https://www.readbyqxmd.com/read/27789915/multipollutant-measurement-error-in-air-pollution-epidemiology-studies-arising-from-predicting-exposures-with-penalized-regression-splines
#1
Silas Bergen, Lianne Sheppard, Joel D Kaufman, Adam A Szpiro
Air pollution epidemiology studies are trending towards a multi-pollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize this measurement error and develop an analytic bias correction when using penalized regression splines to predict exposure. Our simulations show bias from multi-pollutant measurement error can be severe, and in opposite directions or simultaneously positive or negative...
November 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27609994/multivariate-emulation-of-computer-simulators-model-selection-and-diagnostics-with-application-to-a-humanitarian-relief-model
#2
Antony M Overstall, David C Woods
We present a common framework for Bayesian emulation methodologies for multivariate output simulators, or computer models, that employ either parametric linear models or non-parametric Gaussian processes. Novel diagnostics suitable for multivariate covariance separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcanic eruption and earthquake, and a sensitivity analysis is conducted to determine the sensitivity of the simulator output to changes in the input variables...
August 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27524839/nonparametric-spatial-models-for-clustered-ordered-periodontal-data
#3
Dipankar Bandyopadhyay, Antonio Canale
Clinical attachment level (CAL) is regarded as the most popular measure to assess periodontal disease (PD). These probed tooth-site level measures are usually rounded and recorded as whole numbers (in mm) producing clustered (site measures within a mouth) error-prone ordinal responses representing some ordering of the underlying PD progression. In addition, it is hypothesized that PD progression can be spatially-referenced, i.e., proximal tooth-sites share similar PD status in comparison to sites that are distantly located...
August 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27134314/bayesian-hierarchical-modelling-for-inferring-genetic-interactions-in-yeast
#4
Jonathan Heydari, Conor Lawless, David A Lydall, Darren J Wilkinson
Quantitative fitness analysis (QFA) is a high throughput experimental and computational methodology for measuring the growth of microbial populations. QFA screens can be used to compare the health of cell populations with and without a mutation in a query gene to infer genetic interaction strengths genomewide, examining thousands of separate genotypes. We introduce Bayesian hierarchical models of population growth rates and genetic interactions that better reflect QFA experimental design than current approaches...
April 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27041773/two-stage-model-for-time-varying-effects-of-zero-in%C3%AF-ated-count-longitudinal-covariates-with-applications-in-health-behaviour-research
#5
Hanyu Yang, Runze Li, Robert A Zucker, Anne Buu
This study proposes a two-stage approach to characterize individual developmental trajectories of health risk behaviors and delineate their time-varying effects on short-term or long-term health outcomes. Our model can accommodate longitudinal covariates with zero-inflated counts and discrete outcomes. The longitudinal data of a well-known study of youth at high risk for substance abuse are presented as a motivating example to demonstrate the effectiveness of the model in delineating critical developmental periods of prevention and intervention...
April 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27041772/a-two-sample-distribution-free-test-for-functional-data-with-application-to-a-diffusion-tensor-imaging-study-of-multiple-sclerosis
#6
Gina-Maria Pomann, Ana-Maria Staicu, Sujit Ghosh
Motivated by an imaging study, this paper develops a nonparametric testing procedure for testing the null hypothesis that two samples of curves observed at discrete grids and with noise have the same underlying distribution. The objective is to formally compare white matter tract profiles between healthy individuals and multiple sclerosis patients, as assessed by conventional diffusion tensor imaging measures. We propose to decompose the curves using functional principal component analysis of a mixture process, which we refer to as marginal functional principal component analysis...
April 1, 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27546913/assessing-systematic-effects-of-stroke-on-motorcontrol-by-using-hierarchical-function-on-scalar-regression
#7
Jeff Goldsmith, Tomoko Kitago
This work is concerned with understanding common population-level effects of stroke on motor control while accounting for possible subject-level idiosyncratic effects. Upper extremity motor control for each subject is assessed through repeated planar reaching motions from a central point to eight pre-specified targets arranged on a circle. We observe the kinematic data for hand position as a bivariate function of time for each reach. Our goal is to estimate the bivariate function-on-scalar regression with subject-level random functional effects while accounting for potential correlation in residual curves; covariates of interest are severity of motor impairment and target number...
February 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27034510/bayesian-group-sequential-clinical-trial-design-using-total-toxicity-burden-and-progression-free-survival
#8
Brian P Hobbs, Peter F Thall, Steven H Lin
Delivering radiation to eradicate a solid tumor while minimizing damage to nearby critical organs remains a challenge. For esophageal cancer, radiation therapy may damage the heart or lungs, and several qualitatively different, possibly recurrent toxicities associated with chemoradiation or surgery may occur, each at two or more possible grades. In this article, we describe a Bayesian group sequential clinical trial design, based on total toxicity burden (TTB) and progression-free survival duration, for comparing two radiation therapy modalities for esophageal cancer...
February 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26877554/a-dose-schedule-finding-design-for-phase-i-ii-clinical-trials
#9
Beibei Guo, Yisheng Li, Ying Yuan
Dose-finding methods aiming at identifying an optimal dose of a treatment with a given schedule may be at a risk of misidentifying the best treatment for patients. In this article we propose a phase I/II clinical trial design to find the optimal dose-schedule combination. We define schedule as the method and timing of administration of a given total dose in a treatment cycle. We propose a Bayesian dynamic model for the joint effects of dose and schedule. The proposed model allows us to borrow strength across dose-schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects...
February 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26877553/a-bayesian-approach-to-estimate-changes-in-condom-use-from-limited-human-immunodeficiency-virus-prevalence-data
#10
J Dureau, K Kalogeropoulos, P Vickerman, M Pickles, M-C Boily
Evaluation of large-scale intervention programmes against human immunodeficiency virus (HIV) is becoming increasingly important, but impact estimates frequently hinge on knowledge of changes in behaviour such as the frequency of condom use over time, or other self-reported behaviour changes, for which we generally have limited or potentially biased data. We employ a Bayesian inference methodology that incorporates an HIV transmission dynamics model to estimate condom use time trends from HIV prevalence data...
February 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27182090/estimating-controlled-direct-effects-of-restrictivefeeding-practices-in-the-early-dieting-in-girls-study
#11
Yeying Zhu, Debashis Ghosh, Donna L Coffman, Jennifer S Savage
In this article, we examine the causal effect of parental restrictive feeding practices on children's weight status. An important mediator is children's self-regulation status. Recent approaches interpret mediation effects based on the potential outcomes framework. Inverse probability weighting based on propensity scores are used to adjust for confounding and reduce the dimensionality of confounders simultaneously. We show that combining machine learning algorithms and logistic regression to estimate the propensity scores can be more accurate and efficient in estimating the controlled direct effects than using logistic regression alone...
January 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26839439/causal-inference-with-longitudinal-outcomes-and-non-ignorable-drop-out-estimating-the-effect-of-living-alone-on-cognitive-decline
#12
Maria Josefsson, Xavier de Luna, Michael J Daniels, Lars Nyberg
In this paper we develop a model to estimate the causal effect of living arrangement (living alone versus living with someone) on cognitive decline based on a 15-year prospective cohort study, where episodic memory function is measured every five years. One key feature of the model is the combination of propensity score matching to balance confounding variables between the two living arrangement groups -in order to reduce bias due to unbalanced covariates at baseline, with a pattern mixture model for longitudinal data -in order to deal with non-ignorable drop-out...
January 1, 2016: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27239073/modelling-the-type-and-timing-of-consecutive-events-application-to-predicting-preterm-birth-in-repeated-pregnancies
#13
Joanna H Shih, Paul S Albert, Pauline Mendola, Katherine L Grantz
Predicting the occurrence and timing of adverse pregnancy events such as preterm birth is an important analytical challenge in obstetrical practice. Developing statistical approaches that can be used to assess the risk and timing of these adverse events will provide clinicians with tools for individualized risk assessment that account for a woman's prior pregnancy history. Often adverse pregnancy outcomes are subject to competing events; for example, interest may focus on the occurrence of pre-eclampsia-related preterm birth, where preterm birth for other reasons may serve as a competing event...
November 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26635421/a-marginalized-zero-inflated-poisson-regression-model-with-random-effects
#14
D Leann Long, John S Preisser, Amy H Herring, Carol E Golin
Public health research often concerns relationships between exposures and correlated count outcomes. When counts exhibit more zeros than expected under Poisson sampling, the zero-inflated Poisson (ZIP) model with random effects may be used. However, the latent class formulation of the ZIP model can make marginal inference on the sampled population challenging. This article presents a marginalized ZIP model with random effects to directly model the mean of the mixture distribution consisting of 'susceptible' individuals and excess zeroes, providing straightforward inference for overall exposure effects...
November 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26538769/modeling-short-and-long-term-characteristics-of-follicle-stimulating-hormone-as-predictors-of-severe-hot-flashes-in-penn-ovarian-aging-study
#15
Bei Jiang, Naisyin Wang, Mary D Sammel, Michael R Elliott
The Penn Ovarian Aging Study tracked a population-based sample of 436 women aged 35-47 years to determine associations between reproductive hormone levels and menopausal symptoms. We develop a joint modeling method that uses the individual-level longitudinal measurements of follicle stimulating hormone (FSH) to predict the risk of severe hot flashes in a manner that distinguishes long-term trends of the mean trajectory, cumulative changes captured by the derivative of mean trajectory, and short-term residual variability...
November 1, 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26692587/heteroscedastic-car-models-for-areally-referenced-temporal-processes-for-analyzing-california-asthma-hospitalization-data
#16
Harrison Quick, Bradley P Carlin, Sudipto Banerjee
Often in regionally aggregated spatiotemporal models, a single variance parameter is used to capture variability in the spatial structure of the model, ignoring the impact that spatially-varying factors may have on the variability in the underlying process. We extend existing methodologies to allow for region-specific variance components in our analysis of monthly asthma hospitalization rates in California counties, introducing a heteroscedastic CAR model that can greatly improve the fit of our spatiotemporal process...
October 1, 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26166904/optimal-retesting-configurations-for-hierarchical-group-testing
#17
Michael S Black, Christopher R Bilder, Joshua M Tebbs
Hierarchical group testing is widely used to test individuals for diseases. This testing procedure works by first amalgamating individual specimens into groups for testing. Groups testing negatively have their members declared negative. Groups testing positively are subsequently divided into smaller subgroups and are then retested to search for positive individuals. In our paper, we propose a new class of informative retesting procedures for hierarchical group testing that acknowledges heterogeneity among individuals...
August 1, 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/26005223/regression-analysis-for-differentially-misclassified-correlated-binary-outcomes
#18
Li Tang, Robert H Lyles, Caroline C King, Joseph W Hogan, Yungtai Lo
In many epidemiological and clinical studies, misclassification may arise in one or several variables, resulting in potentially invalid analytic results (e.g., estimates of odds ratios of interest) when no correction is made. Here we consider the situation in which correlated binary response variables are subject to misclassification. Building upon prior work, we provide an approach to adjust for potentially complex differential misclassification via internal validation sampling applied at multiple study time points...
April 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/25926710/spatially-weighted-functional-clustering-of-river-network-data
#19
R A Haggarty, C A Miller, E M Scott
Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Directed networks present additional challenges in terms of estimating spatial covariance due to their complex structure. Although suitable river network covariance models have been proposed for use with stream distance, where distance is computed along the stream network, these models have not been extended for contexts where the data are functional, as is often the case with environmental data...
April 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/25897179/a-hybrid-model-for-combining-case-control-and-cohort-studies-in-systematic-reviews-of-diagnostic-tests
#20
Yong Chen, Yulun Liu, Jing Ning, Janice Cormier, Haitao Chu
Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy only focus on sensitivity and specificity and ignore the information on disease prevalence contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. In this paper, we propose a hybrid approach that jointly models the disease prevalence along with the diagnostic test sensitivity and specificity in cohort studies, and the sensitivity and specificity in case-control studies...
April 1, 2015: Journal of the Royal Statistical Society. Series C, Applied Statistics
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