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

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https://www.readbyqxmd.com/read/29085158/phase-i-designs-that-allow-for-uncertainty-in-the-attribution-of-adverse-events
#1
Alexia Iasonos, John O'Quigley
In determining dose limiting toxicities in Phase I studies, it is necessary to attribute adverse events (AE) to being drug related or not. Such determination is subjective and may introduce bias. In this paper, we develop methods for removing or at least diminishing the impact of this bias on the estimation of the maximum tolerated dose (MTD). The approach we suggest takes into account the subjectivity in the attribution of AE by using model-based dose escalation designs. The results show that gains can be achieved in terms of accuracy by recovering information lost to biases...
November 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/28943662/causal-mediation-analysis-for-the-cox-proportional-hazards-model-with-a-smooth-baseline-hazard-estimator
#2
Wei Wang, Jeffrey M Albert
An important problem within the social, behavioral, and health sciences is how to partition an exposure effect (e.g. treatment or risk factor) among specific pathway effects and to quantify the importance of each pathway. Mediation analysis based on the potential outcomes framework is an important tool to address this problem and we consider the estimation of mediation effects for the proportional hazards model in this paper. We give precise definitions of the total effect, natural indirect effect, and natural direct effect in terms of the survival probability, hazard function, and restricted mean survival time within the standard two-stage mediation framework...
August 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/28785119/biomarker-detection-and-categorization-in-ribonucleic-acid-sequencing-meta-analysis-using-bayesian-hierarchical-models
#3
Tianzhou Ma, Faming Liang, George Tseng
Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes. As the next-generation sequencing experiments become mature and affordable, increasing number of RNA-seq datasets are available in the public domain. The count-data based technology provides better experimental accuracy, reproducibility and ability to detect low-expressed genes. A naive approach to combine multiple RNA-seq studies is to apply differential analysis tools such as edgeR and DESeq to each study and then combine the summary statistics of p-values or effect sizes by conventional meta-analysis methods...
August 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/28706323/exploring-the-existence-of-a-stayer-population-with-mover-stayer-counting-process-models-application-to-joint-damage-in-psoriatic-arthritis
#4
Sean Yiu, Vernon T Farewell, Brian D M Tom
Many psoriatic arthritis patients do not progress to permanent joint damage in any of the 28 hand joints, even under prolonged follow-up. This has led several researchers to fit models that estimate the proportion of stayers (those who do not have the propensity to experience the event of interest) and to characterize the rate of developing damaged joints in the movers (those who have the propensity to experience the event of interest). However, when fitted to the same data, the paper demonstrates that the choice of model for the movers can lead to widely varying conclusions on a stayer population, thus implying that, if interest lies in a stayer population, a single analysis should not generally be adopted...
August 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/28255183/parametric-dose-standardization-for-optimizing-two-agent-combinations-in-a-phase-i-ii-trial-with-ordinal-outcomes
#5
Peter F Thall, Hoang Q Nguyen, Ralph G Zinner
A Bayesian model and design are described for a phase I-II trial to jointly optimise the doses of a targeted agent and a chemotherapy agent for solid tumors. A challenge in designing the trial was that both the efficacy and 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 a generalised continuation ratio model was assumed, with each agent's dose parametrically standardised in the linear term...
January 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/28239197/linear-regression-with-a-randomly-censored-covariate-application-to-an-alzheimer-s-study
#6
Folefac D Atem, Jing Qian, Jacqueline E Maye, Keith A Johnson, Rebecca A Betensky
The association between maternal age of onset of dementia and amyloid deposition (measured by in vivo positron emission tomography (PET) imaging) in cognitively normal older offspring is of interest. In a regression model for amyloid, special methods are required due to the random right censoring of the covariate of maternal age of onset of dementia. Prior literature has proposed methods to address the problem of censoring due to assay limit of detection, but not random censoring. We propose imputation methods and a survival regression method that do not require parametric assumptions about the distribution of the censored covariate...
2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/28239196/a-novel-principal-component-analysis-for-spatially-misaligned-multivariate-air-pollution-data
#7
Roman A Jandarov, Lianne A Sheppard, Paul D Sampson, Adam A Szpiro
We propose novel methods for predictive (sparse) PCA 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 the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used...
January 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://www.readbyqxmd.com/read/27789915/multipollutant-measurement-error-in-air-pollution-epidemiology-studies-arising-from-predicting-exposures-with-penalized-regression-splines
#8
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/28461708/bayesian-inference-for-intratumour-heterogeneity-in-mutations-and-copy-number-variation
#9
Juhee Lee, Peter Müller, Subhajit Sengupta, Kamalakar Gulukota, Yuan Ji
Tumor samples are heterogeneous. They consist of different subclones that are characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Heterogeneity can be measured through the identification of the subclonal copy number and sequence at a selected set of loci. Understanding that the accurate identification of variant allele fractions greatly depends on a precise determination of copy numbers, we develop a Bayesian feature allocation model for jointly calling subclonal copy numbers and the corresponding allele sequences for the same loci...
August 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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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