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

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https://read.qxmd.com/read/30880843/a-bayesian-model-free-approach-to-combination-therapy-phase-i-trials-using-censored-time-to-toxicity-data
#1
Graham M Wheeler, Michael J Sweeting, Adrian P Mander
The product of independent beta probabilities escalation (PIPE) design for dual-agent phase I dose-escalation trials is a Bayesian model-free approach for identifying multiple maximum tolerated dose combinations of novel combination therapies. Despite only being published in 2015, the PIPE design has been implemented in at least two oncology trials. However, these trials require patients to have completed follow-up before clinicians can make dose-escalation decisions. For trials of radiotherapy or advanced therapeutics, this may lead to impractically long trial durations due to late-onset treatment-related toxicities...
February 2019: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30745708/two-stage-design-for-phase-i-ii-cancer-clinical-trials-using-continuous-dose-combinations-of-cytotoxic-agents
#2
Mourad Tighiouart
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 of the model parameters. In stage II, we propose a Bayesian adaptive design for conducting the phase II trial to determine dose combination regions along the MTD curve with a desired level of efficacy...
January 2019: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30636815/distributed-lag-interaction-models-with-two-pollutants
#3
Yin-Hsiu Chen, Bhramar Mukherjee, Veronica J Berrocal
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 only consider one pollutant at a time. In this article, we propose distributed lag interaction model (DLIM) to characterize the joint lagged effect of two pollutants. One natural way to model the interaction surface is by assuming that the underlying basis functions are tensor products of the basis functions that generate the main-effect distributed lag functions...
January 2019: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30546161/constructing-treatment-decision-rules-based-on-scalar-and-functional-predictors-when-moderators-of-treatment-effect-are-unknown
#4
Adam Ciarleglio, Eva Petkova, Todd Ogden, Thaddeus Tarpey
Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To better understand this heterogeneity and to help in determining the best patient-specific treatments for a given disease, many clinical trials are collecting large amounts of patient-level data prior to administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images...
November 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30420787/radiologic-image-based-statistical-shape-analysis-of-brain-tumours
#5
Karthik Bharath, Sebastian Kurtek, Arvind Rao, Veerabhadran Baladandayuthapani
We propose a curve-based Riemannian geometric approach for general shape-based statistical analyses of tumours obtained from radiologic images. A key component of the framework is a suitable metric that enables comparisons of tumour shapes, provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumour shapes and allows for a rich class of continuous deformations of a tumour shape. The utility of the framework is illustrated through specific statistical tasks on a data set of radiologic images of patients diagnosed with glioblastoma multiforme , a malignant brain tumour with poor prognosis...
November 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30344346/the-role-of-secondary-outcomes-in-multivariate-meta-analysis
#6
John B Copas, Dan Jackson, Ian R White, Richard D Riley
Univariate meta-analysis concerns a single outcome of interest measured across a number of independent studies. However, many research studies will have also measured secondary outcomes. Multivariate meta-analysis allows us to take these secondary outcomes into account, and can also include studies where the primary outcome is missing. We define the efficiency (E) as the variance of the overall estimate from a multivariate meta-analysis relative to the variance of the overall estimate from a univariate meta-analysis...
November 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30662097/optimal-treatment-allocations-in-space-and-time-for-on-line-control-of-an-emerging-infectious-disease
#7
Eric B Laber, Nick J Meyer, Brian J Reich, Krishna Pacifici, Jaime A Collazo, John M Drake
A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic...
August 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30294047/discussion-of-laber-et-al-optimal-treatment-allocations-in-space-and-time-for-on-line-control-of-an-emerging-infectious-disease
#8
Michael T Lawson, Hunyong Cho, Arkopal Choudhury, Yifan Cui, Xiaotong Jiang, Teeranan Pokaprakarn, Michael R Kosorok
No abstract text is available yet for this article.
August 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/30270943/discussion-on-optimal-treatment-allocations-in-space-and-time-for-on-line-control-of-an-emerging-infectious-disease
#9
Seongho Kim, Weng Kee Wong
No abstract text is available yet for this article.
August 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29540937/modelling-time-varying-heterogeneity-in-recurrent-infection-processes-an-application-to-serological-data
#10
Steven Abrams, Andreas Wienke, Niel Hens
Frailty models are often used in survival analysis to model multivariate time-to-event data. In infectious disease epidemiology, frailty models have been proposed to model heterogeneity in the acquisition of infection and to accommodate association in the occurrence of multiple types of infection. Although traditional frailty models rely on the assumption of lifelong immunity after recovery, refinements have been made to account for reinfections with the same pathogen. Recently, Abrams and Hens quantified the effect of misspecifying the underlying infection process on the basic and effective reproduction number in the context of bivariate current status data on parvovirus B19 and varicella zoster virus...
April 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29430064/survival-analysis-with-functions-of-mismeasured-covariate-histories-the-case-of-chronic-air-pollution-exposure-in-relation-to-mortality-in-the-nurses-health-study
#11
Xiaomei Liao, Xin Zhou, Molin Wang, Jaime E Hart, Francine Laden, Donna Spiegelman
Environmental epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories, such as the 12-month moving average, in relation to chronic disease incidence or mortality. The individual exposure measurements that comprise such an exposure history are usually mis-measured, at least moderately, and, often, more substantially. To obtain unbiased estimates of Cox model hazard ratios for these complex mis-measured exposure functions, an extended risk set regression calibration method for Cox models is developed and applied to a study of long-term exposure to the fine particulate matter ( PM 2...
February 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29371746/clustered-multistate-models-with-observation-level-random-effects-mover-stayer-effects-and-dynamic-covariates-modelling-transition-intensities-and-sojourn-times-in-a-study-of-psoriatic-arthritis
#12
Sean Yiu, Vernon T Farewell, Brian D M Tom
In psoriatic arthritis, it is important to understand the joint activity (represented by swelling and pain) and damage processes because both are related to severe physical disability. The paper aims to provide a comprehensive investigation into both processes occurring over time, in particular their relationship, by specifying a joint multistate model at the individual hand joint level, which also accounts for many of their important features. As there are multiple hand joints, such an analysis will be based on the use of clustered multistate models...
February 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29540936/bayesian-mixed-treatment-comparisons-meta-analysis-for-correlated-outcomes-subject-to-reporting-bias
#13
Yulun Liu, Stacia M DeSantis, Yong Chen
Many randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of RCTs have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared to univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e., when the number of treatments is two)...
January 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29531406/pattern-mixture-models-with-incomplete-informative-cluster-size-application-to-a-repeated-pregnancy-study
#14
Ashok Chaurasia, Danping Liu, Paul S Albert
The incomplete informative cluster size problem is motivated by the NICHD Consecutive Pregnancies Study, aiming to study the relationship between pregnancy outcomes and parity. These pregnancy outcomes are potentially associated with the number of births over a woman's lifetime, resulting in an incomplete informative cluster size (censored at the end of the study window). We develop a pattern mixture model for informative cluster size by treating the lifetime number of births as a latent variable. We compare this approach with a simple alternative method that approximates the pattern mixture model...
January 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29353941/a-bayesian-model-selection-approach-for-identifying-differentially-expressed-transcripts-from-rna-sequencing-data
#15
Panagiotis Papastamoulis, Magnus Rattray
Recent advances in molecular biology allow the quantification of the transcriptome and scoring transcripts as differentially or equally expressed between two biological conditions. Although these two tasks are closely linked, the available inference methods treat them separately: a primary model is used to estimate expression and its output is post processed by using a differential expression model. In the paper, both issues are simultaneously addressed by proposing the joint estimation of expression levels and differential expression: the unknown relative abundance of each transcript can either be equal or not between two conditions...
January 2018: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29249839/a-calibrated-power-prior-approach-to-borrow-information-from-historical-data-with-application-to-biosimilar-clinical-trials
#16
Haitao Pan, Ying Yuan, Jielai Xia
A biosimilar refers to a follow-on biologic intended to be approved for marketing based on biosimilarity to an existing patented biological product (i.e., the reference product). To develop a biosimilar product, it is essential to demonstrate biosimilarity between the follow-on biologic and the reference product, typically through two-arm randomization trials. We propose a Bayesian adaptive design for trials to evaluate biosimilar products. To take advantage of the abundant historical data on the efficacy of the reference product that is typically available at the time a biosimilar product is developed, we propose the calibrated power prior, which allows our design to adaptively borrow information from the historical data according to the congruence between the historical data and the new data collected from the current trial...
November 2017: Journal of the Royal Statistical Society. Series C, Applied Statistics
https://read.qxmd.com/read/29085158/phase-i-designs-that-allow-for-uncertainty-in-the-attribution-of-adverse-events
#17
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://read.qxmd.com/read/28943662/causal-mediation-analysis-for-the-cox-proportional-hazards-model-with-a-smooth-baseline-hazard-estimator
#18
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://read.qxmd.com/read/28785119/biomarker-detection-and-categorization-in-ribonucleic-acid-sequencing-meta-analysis-using-bayesian-hierarchical-models
#19
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://read.qxmd.com/read/28706323/exploring-the-existence-of-a-stayer-population-with-mover-stayer-counting-process-models-application-to-joint-damage-in-psoriatic-arthritis
#20
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
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