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Causal mechanism of extreme river discharges in the upper Danube basin network J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200523
Linda Mhalla; Valérie Chavez‐Demoulin; Debbie J. DupuisExtreme hydrological events in the Danube river basin may severely impact human populations, aquatic organisms and economic activity. One often characterizes the joint structure of extreme events by using the theory of multivariate and spatial extremes and its asymptotically justified models. There is interest, however, in cascading extreme events and whether one event causes another. We argue that

The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200522
Francesco Schirripa Spagnolo; Nicola Salvati; Antonella D’Agostino; Ides NicaiseM ‐quantile random‐effects regression represents an interesting approach for modelling multilevel data when the researcher is focused on conditional quantiles. When data are obtained from complex survey designs, sampling weights must be incorporated in the analysis. A robust pseudolikelihood approach for accommodating sampling weights in M ‐quantile random‐effects regression is presented. In particular

A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200522
Yanqing Sun; Li Qi; Fei Heng; Peter B. GilbertDeployment of the recently licensed tetravalent dengue vaccine based on a chimeric yellow fever virus, CYD‐TDV, requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titre—neutralizing antibodies) and on a ‘mark’ feature of the dengue disease failure event (the amino acid sequence distance of the

Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200514
Federica Bianchi; Francesco Bartolucci; Stefano Peluso; Antonietta MiraFinancial markets are ultimately seen as a collection of dyadic transactions. We study the temporal evolution of dyadic relationships in the European interbank market, as induced by monetary transactions registered in the electronic market for interbank deposits (e‐MID) during a period of 10 years (2006–2015). In particular, we keep track of how reciprocal exchange patterns have varied with macro events

Small sample corrections for Wald tests in latent variable models J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200513
Brice Ozenne; Patrick M. Fisher; Esben Budtz‐J⊘rgensenLatent variable models are commonly used in psychology and increasingly used for analysing brain imaging data. Such studies typically involve a small number of participants (n<100), where standard asymptotic results often fail to control the type 1 error appropriately. The paper presents two corrections improving the control of the type 1 error of Wald tests in latent variable models estimated by using

Statistical inference on tree swallow migrations with random forests J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200509
Tim Coleman; Lucas Mentch; Daniel Fink; Frank A. La Sorte; David W. Winkler; Giles Hooker; Wesley M. HochachkaBird species’ migratory patterns have typically been studied through individual observations and historical records. In recent years, the eBird citizen science project, which solicits observations from thousands of bird watchers around the world, has opened the door for a data‐driven approach to understanding the large‐scale geographical movements. Here, we focus on the North American tree swallow

Variable selection in functional linear concurrent regression J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200504
Rahul Ghosal; Arnab Maity; Timothy Clark; Stefano B. LongoWe propose a novel method for variable selection in functional linear concurrent regression. Our research is motivated by a fisheries footprint study where the goal is to identify important time‐varying sociostructural drivers influencing patterns of seafood consumption, and hence the fisheries footprint, over time, as well as estimating their dynamic effects. We develop a variable‐selection method

Generalized partially linear models on Riemannian manifolds J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200503
Amelia Simó; M. Victoria Ibáñez; Irene Epifanio; Vicent GimenoWe introduce generalized partially linear models with covariates on Riemannian manifolds. These models, like ordinary generalized linear models, are a generalization of partially linear models on Riemannian manifolds that allow for scalar response variables with error distribution models other than a normal distribution. Partially linear models are particularly useful when some of the covariates of

The harmonic mean χ2‐test to substantiate scientific findings J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200430
Leonhard HeldStatistical methodology plays a crucial role in drug regulation. Decisions by the US Food and Drug Administration or European Medicines Agency are typically made based on multiple primary studies testing the same medical product, where the two‐trials rule is the standard requirement, despite shortcomings. A new approach is proposed for this task based on the harmonic mean of the squared study‐specific

Simulating gene silencing through intervention analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200426
Vera Djordjilović; Monica Chiogna; Chiara RomualdiWe propose a novel method for simulating the effects of gene silencing. Our approach combines relevant subject matter information provided by biological pathways with gene expression levels measured in regular conditions to predict the behaviour of the system after one of the genes has been silenced. We achieve this by modelling gene silencing as an external intervention in a causal graphical model

Modelling the spatial extent and severity of extreme European windstorms J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20191223
Paul Sharkey; Jonathan A. Tawn; Simon J. BrownWindstorms are a primary natural hazard affecting Europe that are commonly linked to substantial property and infrastructural damage and are responsible for the largest spatially aggregated financial losses. Such extreme winds are typically generated by extratropical cyclone systems originating in the North Atlantic and passing over Europe. Previous statistical studies tend to model extreme winds at

The analysis of transformations for profit‐and‐loss data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20191217
Anthony C. Atkinson; Marco Riani; Aldo CorbelliniWe analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on the profitability of 1405 firms, 407 of which report losses. The problem in both cases is to use regression to predict performance from sets of explanatory variables. In one case, it is clear from scatter plots of the data that the negative responses have a lower variance than the positive responses

Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200107
Vincent Chin; David Gunawan; Denzil G. Fiebig; Robert Kohn; Scott A. SissonThe paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo sampling. We also propose

Bayesian protein sequence and structure alignment J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200108
Christopher J. Fallaize; Peter J. Green; Kanti V. Mardia; Stuart BarberThe structure of a protein is crucial in determining its functionality and is much more conserved than sequence during evolution. A key task in structural biology is to compare protein structures to determine evolutionary relationships, to estimate the function of newly discovered structures and to predict unknown structures. We propose a Bayesian method for protein structure alignment, with the prior

Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200123
Martin Jullum; Thordis Thorarinsdottir; Fabian E. BachlThe Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical factors in the estimation of abundance that is needed for management of the species. These estimates are usually based on counts from aerial photographic surveys. However, only a minor part of the whelping region can be photographed, because of its large extent. To estimate

Estimation and inference in mixed effect regression models using shape constraints, with application to tree height estimation J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20191224
Xiyue Liao; Mary C. MeyerEstimation of tree height given diameter is an important part of the forest inventory analysis of the US Forest Service. Existing methods use parametric models to estimate the curve. We propose a semiparametric model in which log (height) is a smooth, increasing and concave function of diameter, with a random‐plot component and fixed effect covariates. Large sample properties and inference methods

Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20191227
Jim E. Griffin; Eleni Matechou; Andrew S. Buxton; Dimitrios Bormpoudakis; Richard A. GriffithsEnvironmental DNA is a survey tool with rapidly expanding applications for assessing the presence of a species at surveyed sites. Environmental DNA methodology is known to be prone to false negative and false positive errors at the data collection and laboratory analysis stages. Existing models for environmental DNA data require augmentation with additional sources of information to overcome identifiability

Using Cox regression to develop linear rank tests with zero‐inflated clustered data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200203
Stuart R. Lipsitz; Garrett M. Fitzmaurice; Debajyoti Sinha; Alexander P. Cole; Christian P. Meyer; Quoc‐Dien TrinhZero‐inflated data arise in many fields of study. When comparing zero‐inflated data between two groups with independent subjects, a 2 degree‐of‐freedom test has been developed, which is the sum of a 1 degree‐of‐freedom Pearson χ2‐test for the 2×2 table of group versus dichotomized outcome (0,>0) and a 1 degree‐of‐freedom Wilcoxon rank sum test for the values of the outcome ‘>0’. Here, we extend this

Assessing heterogeneity in transition propensity in multistate capture–recapture data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20191224
Anita Jeyam; Rachel McCrea; Roger PradelMultistate capture–recapture models are a useful tool to help to understand the dynamics of movement within discrete capture–recapture data. The standard multistate capture–recapture model, however, relies on assumptions of homogeneity within the population with respect to survival, capture and transition probabilities. There are many ways in which this model can be generalized so some guidance on

A flexible parametric modelling framework for survival analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200220
Kevin Burke; M. C. Jones; Angela NoufailyWe introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard functions (constant; increasing; decreasing; up then down; down then up) and various common survival distributions (log‐logistic; Burr type XII; Weibull; Gompertz) and includes defective distributions (cure models). This generality is achieved by using four distributional parameters: two

Bayesian hierarchical modelling of growth curve derivatives via sequences of quotient differences J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200205
Garritt L. Page; María Xosé Rodríguez‐Álvarez; Dae‐Jin LeeGrowth curve studies are typically conducted to evaluate differences between group or treatment‐specific curves. Most analyses focus solely on the growth curves, but it has been argued that the derivative of growth curves can highlight differences between groups that may be masked when considering the raw curves only. Motivated by the desire to estimate derivative curves hierarchically, we introduce

Factor‐augmented Bayesian cointegration models: a case‐study on the soybean crush spread J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200122
Maciej Marowka; Gareth W. Peters; Nikolas Kantas; Guillaume BagnarosaWe investigate how vector auto‐regressive models can be used to study the soybean crush spread. By crush spread we mean a time series marking the difference between a weighted combination of the value of soymeal and soyoil to the value of the original soybeans. Commodity industry practitioners often use fixed prescribed values for these weights, which do not take into account any time‐varying effects

An optimal design for hierarchical generalized group testing J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200422
Yaakov Malinovsky; Gregory Haber; Paul S. AlbertChoosing an optimal strategy for hierarchical group testing is an important problem for practitioners who are interested in disease screening with limited resources. For example, when screening for infectious diseases in large populations, it is important to use algorithms that minimize the cost of potentially expensive assays. Black and co‐workers described this as an intractable problem unless the

A Bayesian group sequential small n sequential multiple‐assignment randomized trial J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200409
Yan‐Cheng Chao; Thomas M. Braun; Roy N. Tamura; Kelley M. KidwellA small n, sequential, multiple‐assignment, randomized trial (called ‘snSMART’) is a small sample multistage design where participants may be rerandomized to treatment on the basis of intermediate end points. This design is motivated by the ‘A randomized multicenter study for isolated skin vasculitis’ trial (NCT02939573): an on‐going snSMART design focusing on the evaluation of three drugs for isolated

A spatially varying distributed lag model with application to an air pollution and term low birth weight study J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200330
Joshua L. Warren; Thomas J. Luben; Howard H. ChangDistributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially

Robust and adaptive anticoagulant control J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200315
Peter Avery; Quentin Clairon; Robin Henderson; C. James Taylor; Emma WilsonWe consider a control theory approach to adaptive dose allocation of anticoagulants, based on an analysis of records of 152 patients on long‐term warfarin treatment. We consider a selection of statistical models for the relationship between the dose of drug and subsequent blood clotting speed, measured through the international normalized ratio. Our main focus is on subsequent use of the model in guiding

A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200315
C. Denis; E. Lebarbier; C. Lévy‐Leduc; O. Martin; L. SansonnetMotivated by an application to the clustering of milking kinetics of dairy goats, we propose a novel approach for functional data clustering. This issue is of growing interest in precision livestock farming, which is largely based on the development of data acquisition automation and on the development of interpretative tools to capitalize on high throughput raw data and to generate benchmarks for

A joint confidence region for an overall ranking of populations J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200312
Martin Klein; Tommy Wright; Jerzy WieczorekNational statistical agencies lack statistical methodology to express uncertainty in their released estimated overall rankings. For example, the US Census Bureau produced an ‘explicit’ ranking of the states based on observed sample estimates during 2011 of mean travel time to work. Current literature provides measures of uncertainty in estimated individual ranks, but not a direct measure of uncertainty

Multiple imputation of binary multilevel missing not at random data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200224
Angelina Hammon; Sabine ZinnWe introduce a selection model‐based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first equation of the model defines the regression model for the missing data mechanism. The second equation specifies

Structured penalized regression for drug sensitivity prediction J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.344) Pub Date : 20200223
Zhi Zhao; Manuela ZucknickLarge‐scale in vitro drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high dimensional heterogeneous multiomics data as input data and with potentially strong correlations between the outcome variables which

Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20191126
Vinny Davies,Umberto Noè,Alan Lazarus,Hao Gao,Benn Macdonald,Colin Berry,Xiaoyu Luo,Dirk HusmeierA central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution

Improving the identification of antigenic sites in the H1N1 influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20191011
Vinny Davies,William T Harvey,Richard Reeve,Dirk HusmeierUnderstanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the influenza A(H1N1) virus through the prediction of haemagglutination inhibition (HI) titre: pairwise

Bayesian nonparametric survival regression for optimizing precision dosing of intravenous busulfan in allogeneic stem cell transplantation. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190831
Yanxun Xu,Peter F Thall,William Hua,Borje S AnderssonAllogeneic stem cell transplantation (alloSCT) is now part of standard of care for acute leukemia (AL). To reduce toxicity of the pretransplant conditioning regimen, intravenous busulfan is usually used as a preparative regimen for AL patients undergoing alloSCT. Systemic busulfan exposure, characterized by the area under the plasma concentration versus time curve (AUC), is strongly associated with

Landmark Linear Transformation Model for Dynamic Prediction with Application to a Longitudinal Cohort Study of Chronic Disease. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190831
Yayuan Zhu,Liang Li,Xuelin HuangDynamic prediction of the risk of a clinical event using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a linear transformation model, but allows all the model parameters to vary with the landmark time. This model includes many published landmark prediction models as

Additive quantile regression for clustered data with an application to children's physical activity. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190801
Marco GeraciAdditive models are flexible regression tools that handle linear as well as nonlinear terms. The latter are typically modelled via smoothing splines. Additive mixed models extend additive models to include random terms when the data are sampled according to cluster designs (e.g. longitudinal).These models find applications in the study of phenomena like growth, certain disease mechanisms and energy

Comment on Laber et al. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190622
M Elizabeth Halloran,Michael G Hudgens 
AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dualagent dose finding trials. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190614
Jiaying Lyu,Yuan Ji,Naiqing Zhao,Daniel V T CatenacciWe propose a flexible design for the identification of optimal dose combinations in dualagent 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 dualagent dose finding and are supported by the numerical results presented

Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190521
Juhee Lee,Peter F Thall,Katy RezvaniA sequentially adaptive Bayesian design is presented for a clinical trial of cord blood derived natural killer cells to treat severe hematologic malignancies. Given six prognostic subgroups defined by disease type and severity, the goal is to optimize cell dose in each subgroup. The trial has five coprimary outcomes, the times to severe toxicity, cytokine release syndrome, disease progression or response

Correlated multistate models for multiple processes: an application to renal disease progression in systemic lupus erythematosus. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190521
Aidan G O'Keeffe,Li Su,Vernon T FarewellBidirectional changes over time in the estimated glomerular filtration rate and in urine protein content are of interest for the treatment and management of patients with lupus nephritis. Although these processes may be modelled by separate multistate models, the processes are likely to be correlated within patients. Motivated by the lupus nephritis application, we develop a new multistate modelling

An information theoretic phase III design for molecularly targeted agents that does not require an assumption of monotonicity. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190423
Pavel Mozgunov,Thomas JakiFor many years phase I and phase II clinical trials have been conducted separately, but there has been a recent shift to combine these phases. Although a variety of phase III modelbased designs for cytotoxic agents have been proposed in the literature, methods for molecularly targeted agents (TAs) are just starting to develop. The main challenge of the TA setting is the unknown doseefficacy relationship

Bayesian logGaussian Cox process regression: with applications to metaanalysis of neuroimaging working memory studies. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190325
Pantelis Samartsidis,Claudia R Eickhoff,Simon B Eickhoff,Tor D Wager,Lisa Feldman Barrett,Shir Atzil,Timothy D Johnson,Thomas E NicholsWorking memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging (fMRI). With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for metaanalysis to identify the brain regions consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot

Optimal treatment allocations in space and time for online control of an emerging infectious disease. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20190122
Eric B Laber,Nick J Meyer,Brian J Reich,Krishna Pacifici,Jaime A Collazo,John M DrakeA 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 uptodate information on the spread of an infectious disease to a subset of locations where treatment

Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20181214
Adam Ciarleglio,Eva Petkova,Todd Ogden,Thaddeus TarpeyTreatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To better understand this heterogeneity and to help in determining the best patientspecific treatments for a given disease, many clinical trials are collecting large amounts of patientlevel data prior to administering treatment in the hope that some of these data can be used to identify moderators

A Bayesian modelfree approach to combination therapy phase I trials using censored timetotoxicity data. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20181122
Graham M Wheeler,Michael J Sweeting,Adrian P ManderThe product of independent beta probabilities escalation (PIPE) design for dualagent phase I doseescalation trials is a Bayesian modelfree 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

Radiologic imagebased statistical shape analysis of brain tumours. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20181114
Karthik Bharath,Sebastian Kurtek,Arvind Rao,Veerabhadran BaladandayuthapaniWe propose a curvebased Riemannian geometric approach for general shapebased 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

The role of secondary outcomes in multivariate metaanalysis. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20181023
John B Copas,Dan Jackson,Ian R White,Richard D RileyUnivariate metaanalysis concerns a single outcome of interest measured across a number of independent studies. However, many research studies will have also measured secondary outcomes. Multivariate metaanalysis 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

Discussion of Laber et al. "Optimal treatment allocations in space and time for online control of an emerging infectious disease". J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20181009
Michael T Lawson,Hunyong Cho,Arkopal Choudhury,Yifan Cui,Xiaotong Jiang,Teeranan Pokaprakarn,Michael R Kosorok 
Discussion on Optimal treatment allocations in space and time for online control of an emerging infectious disease. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20181003
Seongho Kim,Weng Kee Wong 
Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180813
Wenting Cheng,Jeremy M G Taylor,Tian Gu,Scott A Tomlins,Bhramar MukherjeeWe consider a situation where there is rich historical data 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

Distributed Lag Interaction Models with Two Pollutants. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180708
YinHsiu Chen,Bhramar Mukherjee,Veronica J BerrocalDistributed 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

Twostage design for phase III cancer clinical trials using continuous dose combinations of cytotoxic agents. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180622
Mourad TighiouartWe present a twostage 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

Bayesian mixed treatment comparisons metaanalysis for correlated outcomes subject to reporting bias. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180316
Yulun Liu,Stacia M DeSantis,Yong ChenMany randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate metaanalytic 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 metaanalysis. However, most methods for multivariate

Modelling time varying heterogeneity in recurrent infection processes: an application to serological data. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180316
Steven Abrams,Andreas Wienke,Niel HensFrailty models are often used in survival analysis to model multivariate timetoevent 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

Patternmixture models with incomplete informative cluster size: Application to a repeated pregnancy study. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180314
Ashok Chaurasia,Danping Liu,Paul S AlbertThe 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

Mediation analysis for count and zeroinflated count data without sequential ignorability and its application in dental studies. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180201
Zijian Guo,Dylan S Small,Stuart A Gansky,Jing ChengMediation analysis seeks to understand the mechanism by which a treatment affects an outcome. Count or zeroinflated count outcomes are common in many studies in which mediation analysis is of interest. For example, in dental studies, outcomes such as the number of decayed, missing and filled teeth are typically zero inflated. Existing mediation analysis approaches for count data often assume sequential

Clustered multistate models with observation level random effects, moverstayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180127
Sean Yiu,Vernon T Farewell,Brian D M TomIn 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

A Bayesian model selection approach for identifying differentially expressed transcripts from RNA sequencing data. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20180123
Panagiotis Papastamoulis,Magnus RattrayRecent 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

Accommodating informative dropout and death: a joint modelling approach for longitudinal and semicompeting risks data. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20171227
Qiuju Li,Li SuBoth dropout and death can truncate observation of a longitudinal outcome. Since extrapolation beyond death is often not appropriate, it is desirable to obtain the longitudinal outcome profile of a population given being alive. We propose a new likelihoodbased approach to accommodating informative dropout and death by jointly modelling the longitudinal outcome and semicompeting event times of dropout

A Calibrated Power Prior Approach to Borrow Information from Historical Data with Application to Biosimilar Clinical Trials. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20171219
Haitao Pan,Ying Yuan,Jielai XiaA biosimilar refers to a followon 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 followon biologic and the reference product, typically through twoarm randomization trials. We propose a Bayesian adaptive design

Phase I Designs that Allow for Uncertainty in the Attribution of Adverse Events. J. R. Stat. Soc. Ser. C Appl. Stat. Pub Date : 20171101
Alexia Iasonos,John O'QuigleyIn 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