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Causal inference, social networks and chain graphs J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200718
Elizabeth L. Ogburn; Ilya Shpitser; Youjin LeeTraditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and

Simple rules to guide expert classifications J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200527
Jongbin Jung; Connor Concannon; Ravi Shroff; Sharad Goel; Daniel G. GoldsteinJudges, doctors and managers are among those decision makers who must often choose a course of action under limited time, with limited knowledge and without the aid of a computer. Because data‐driven methods typically outperform unaided judgements, resource‐constrained practitioners can benefit from simple, statistically derived rules that can be applied mentally. In this work, we formalize long‐standing

A similarity‐based approach for macroeconomic forecasting J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200608
Y. Dendramis; G. Kapetanios; M. MarcellinoIn the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behaviour is subject to abrupt changes, associated for example with crisis periods. We propose similarity‐based approaches as a way to handle parameter instability and apply them to macroeconomic forecasting. The rationale is that clusters of past data that

Forecasting of cohort fertility under a hierarchical Bayesian approach J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200417
Joanne Ellison; Erengul Dodd; Jonathan J. ForsterFertility projections are a key determinant of population forecasts, which are widely used by government policy makers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a data set from the human fertility database, we obtain fertility forecasts for 30 countries

Fulfilling the information need after an earthquake: statistical modelling of citizen science seismic reports for predicting earthquake parameters in near realtime J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200530
Francesco FinazziWhen an earthquake affects an inhabited area, a need for information immediately arises among the population. In general, this need is not immediately fulfilled by official channels which usually release expert‐validated information with delays of many minutes. Seismology is among the research fields where citizen science projects succeeded in collecting useful scientific information. More recently

Change point analysis of historical battle deaths J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200610
Brennen T. Fagan; Marina I. Knight; Niall J. MacKay; A. Jamie WoodIt has been claimed and disputed that World War II has been followed by a ‘long peace’: an unprecedented decline of war. We conduct a full change point analysis of well‐documented, publicly available battle deaths data sets, using new techniques that enable the robust detection of changes in the statistical properties of such heavy‐tailed data. We first test and calibrate these techniques. We then

Finding the strength in a weak instrument in a study of cognitive outcomes produced by Catholic high schools J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200413
Siyu Heng; Dylan S. Small; Paul R. RosenbaumWe show that the strength of an instrument is incompletely characterized by the proportion of compliers, and we propose and evaluate new methods that extract more information from certain settings with comparatively few compliers. Specifically, we demonstrate that, for a fixed small proportion of compliers, the presence of an equal number of always‐takers and never‐takers weakens an instrument, whereas

A placebo design to detect spillovers from an education–entertainment experiment in Uganda J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200519
Anna M. Wilke; Donald P. Green; Jasper CooperEducation–entertainment refers to dramatizations designed to convey information and to change attitudes. Buoyed by observational studies suggesting that education–entertainment strongly influences beliefs, attitudes and behaviours, scholars have recently assessed education–entertainment by using rigorous experimental designs in field settings. Studies conducted in developing countries have repeatedly

New statistical metrics for multisite replication projects J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200521
Maya B. Mathur; Tyler J. VanderWeeleIncreasingly, researchers are attempting to replicate published original studies by using large, multisite replication projects, at least 134 of which have been completed or are on going. These designs are promising to assess whether the original study is statistically consistent with the replications and to reassess the strength of evidence for the scientific effect of interest. However, existing

Multilevel network meta‐regression for population‐adjusted treatment comparisons J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200607
David M. Phillippo; Sofia Dias; A. E. Ades; Mark Belger; Alan Brnabic; Alexander Schacht; Daniel Saure; Zbigniew Kadziola; Nicky J. WeltonStandard network meta‐analysis (NMA) and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. Population adjustment methods relax this assumption using individual patient data from one or more studies. However, current matching‐adjusted indirect comparison and simulated treatment comparison methods

Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value‐added tax data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200421
Paul Labonne; Martin WealeThe paper derives monthly estimates of business sector output in the UK from rolling quarterly value‐added tax based turnover data. The administrative nature of the value‐added tax data implies that their use could ultimately yield a more precise and granular picture of output across the economy. However, they show two particular features which complicate their exploitation: they are overlapping and

Model‐based clustering and analysis of life history data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200523
Marc A. Scott; Kaushik Mohan; Jacques‐Antoine GauthierMethods and models for longitudinal data with categorical, multi‐dimensional outcomes are quite limited, but they are essential to the study of life histories. For example, in the Swiss Household Panel, information on the co‐residence and professional status of several thousand individuals is available through to age 45 years. Interest centres on the time and order of life course events such as having

A causal inference framework for cancer cluster investigations using publicly available data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200425
Rachel C. Nethery; Yue Yang; Anna J. Brown; Francesca DominiciOften, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the US Centers for Disease Control and Prevention recommend that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach

Improving external validity of epidemiologic cohort analyses: a kernel weighting approach J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200425
Lingxiao Wang; Barry I. Graubard; Hormuzd A. Katki; and Yan LiFor various reasons, cohort studies generally forgo probability sampling required to obtain population representative samples. However, such cohorts lack population representativeness, which invalidates estimates of population prevalences for novel health factors that are only available in cohorts. To improve external validity of estimates from cohorts, we propose a kernel weighting (KW) approach that

Obituaries J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200618
T. M. F. Smith, 1934–2019 Terence Michael Frederick Smith, born January 18th, 1934, died December 7th, 2019. Terence Michael Frederick Smith: Michael to his original family and Fred to the rest of the world, was born in Ilford into a family that had no history of high educational attainment. At 11 years of age he won a scholarship to the local grammar school which made him one of the first to benefit

Brand versus generic: addressing non‐adherence, secular trends and non‐overlap J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200520
Lamar Hunt; Irene B. Murimi; Jodi B. Segal; Marissa J. Seamans; Daniel O. Scharfstein; and Ravi VaradhanWhereas generic drugs offer a cost‐effective alternative to brand name drugs, regulators need a method to assess therapeutic equivalence in a post‐market setting. We develop such a method in the context of assessing the therapeutic equivalence of immediate release venlafaxine, based on a large insurance claims data set provided by OptumLabs\circledR. To address this question properly, our methodology

A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200508
Pantelis Samartsidis; Shaun R. Seaman; Silvia Montagna; André Charlett; Matthew Hickman; Daniela De AngelisA problem that is frequently encountered in many areas of scientific research is that of estimating the effect of a non‐randomized binary intervention on an outcome of interest by using time series data on units that received the intervention (‘treated’) and units that did not (‘controls’). One popular estimation method in this setting is based on the factor analysis (FA) model. The FA model is fitted

Examining the causal mediating role of brain pathology on the relationship between diabetes and cognitive impairment: the Cardiovascular Health Study J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200508
Ryan M. Andrews; Ilya Shpitser; Oscar Lopez; William T. Longstreth; Paulo H. M. Chaves; Lew Kuller; Michelle C. CarlsonThe paper examines whether diabetes mellitus leads to incident mild cognitive impairment and dementia through brain hypoperfusion and white matter disease. We performed inverse odds ratio weighted causal mediation analyses to decompose the effect of diabetes on cognitive impairment into direct and indirect effects, and we found that approximately a third of the total effect of diabetes is mediated

Instrumental variable methods using dynamic interventions J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200430
Jacqueline A. Mauro; Edward H. Kennedy; Daniel NaginRecent work on dynamic interventions has greatly expanded the range of causal questions that researchers can study. Simultaneously, this work has weakened identifying assumptions, yielding effects that are more practically relevant. Most work in dynamic interventions to date has focused on settings where we directly alter some unconfounded treatment of interest. In policy analysis, decision makers

Causal discovery of gene regulation with incomplete data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200418
Ronja Foraita; Juliane Friemel; Kathrin Günther; Thomas Behrens; Jörn Bullerdiek; Rolf Nimzyk; Wolfgang Ahrens; Vanessa DidelezCausal discovery algorithms aim to identify causal relations from observational data and have become a popular tool for analysing genetic regulatory systems. In this work, we applied causal discovery to obtain novel insights into the genetic regulation underlying head‐and‐neck squamous cell carcinoma. Some methodological challenges needed to be resolved first. The available data contained missing values

Causes of effects via a Bayesian model selection procedure J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200320
Fabio Corradi; Monica MusioIn causal inference, and specifically in the causes‐of‐effects problem, one is interested in how to use statistical evidence to understand causation in an individual case, and in particular how to assess the so‐called probability of causation. The answer involves the use of potential responses, which describe what would have happened to the outcome if we had observed a different value for the exposure

Direct and stable weight adjustment in non‐experimental studies with multivalued treatments: analysis of the effect of an earthquake on post‐traumatic stress J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200413
María de los Angeles Resa; José R. ZubizarretaIn February 2010, a massive earthquake struck Chile, causing devastation in certain parts of the country, affecting other areas, and leaving territories untouched. 2 months after the earthquake, Chile's Ministry of Social Development reinterviewed a representative subsample of its National Socioeconomic Characterization Survey, which had been completed 2 months before the earthquake, thereby creating

Gender differences in the perception of safety in public transport J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200324
Laila Ait Bihi Ouali; Daniel J. Graham; Alexander Barron; Mark TrompetConcerns over women's safety on public transport systems are commonly reported in the media. We develop statistical models to test for gender differences in the perception of safety and satisfaction on urban metros and buses by using large‐scale unique customer satisfaction data for 28 world cities over the period 2009–2018. Results indicate a significant gender gap in the perception of safety, with

A functional approach to small area estimation of the relative median poverty gap J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200323
Enrico Fabrizi; Maria Rosaria Ferrante; Carlo TrivisanoWe consider the estimation of the relative median poverty gap (RMPG) at the level of Italian provinces by using data from the European Union Survey on Income and Living Conditions. The overall sample size does not allow reliable estimation of income‐distribution‐related parameters at the provincial level; therefore, small area estimation techniques must be used. The specific challenge in estimating

How does temperature vary over time?: evidence on the stationary and fractal nature of temperature fluctuations J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200320
John K. Dagsvik; Mariachiara Fortuna; Sigmund Hov MoenThe paper analyses temperature data from 96 selected weather stations world wide, and from reconstructed northern hemisphere temperature data over the last two millennia. Using a non‐parametric test, we find that the stationarity hypothesis is not rejected by the data. Subsequently, we investigate further properties of the data by means of a statistical model known as the fractional Gaussian noise

Selecting a scale for spatial confounding adjustment J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200311
Joshua P. Keller; Adam A. SzpiroUnmeasured, spatially structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment

Multiple‐systems analysis for the quantification of modern slavery: classical and Bayesian approaches J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200305
Bernard W. SilvermanMultiple‐systems estimation is a key approach for quantifying hidden populations such as the number of victims of modern slavery. The UK Government published an estimate of 10000–13000 victims, constructed by the present author, as part of the strategy leading to the Modern Slavery Act 2015. This estimate was obtained by a stepwise multiple‐systems method based on six lists. Further investigation shows

Longevity forecasting by socio‐economic groups using compositional data analysis J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200302
S⊘ren Kjærgaard; Yunus Emre Ergemen; Marie‐Pier Bergeron‐Boucher; Jim Oeppen; Malene Kallestrup‐LambSeveral Organisation for Economic Co‐operation and Development countries have recently implemented an automatic link between the statutory retirement age and life expectancy for the total population to ensure sustainability in their pension systems due to increasing life expectancy. As significant mortality differentials are observed across socio‐economic groups, future changes in these differentials

A novel approach to latent class modelling: identifying the various types of body mass index individuals J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200225
Sarah Brown; William Greene; Mark HarrisGiven the increasing prevalence of adult obesity, furthering understanding of the determinants of measures such as the body mass index (BMI ) remains high on the policy agenda. We contribute to existing literature on modelling the BMI by proposing an extension to latent class modelling, which serves to unveil a more detailed picture of the determinants of BMI. Interest here lies in latent class analysis

Freight rates in downside and upside markets: pricing of own and spillover risks from other shipping segments J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200225
Panayiotis Theodossiou; Dimitris Tsouknidis; Christos SavvaShipping freight rates are notoriously volatile and shipping investors are perceived to be risk loving. The paper explores the stochastic properties of freight rates in the shipping industry and derives the analytical equations for their moments in downside and upside markets by using a two‐piece extension of the generalized error distribution. Pricing equations developed across shipping segments show

A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200225
Yajing Zhu; Fiona Steele; Irini MoustakiWe propose a multilevel structural equation model to investigate the interrelationships between childhood socio‐economic circumstances, partnership formation and stability, and mid‐life health, using data from the 1958 British birth cohort. The structural equation model comprises latent class models that characterize the patterns of change in four dimensions of childhood socio‐economic circumstances

Spatial confounding in hurdle multilevel beta models: the case of the Brazilian Mathematical Olympics for Public Schools J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200220
João B. M. Pereira; Widemberg S. Nobre; Igor F. L. Silva; Alexandra M. SchmidtAmong the many disparities for which Brazil is known is the difference in performance across students who attend the three administrative levels of Brazilian public schools: federal, state and municipal. Our main goal is to investigate whether student performance in the Brazilian Mathematical Olympics for Public Schools is associated with school administrative level and student gender. For this, we

Discovering causal structures in Bayesian Gaussian directed acyclic graph models J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200203
Federico Castelletti; Guido ConsonniCausal directed acyclic graphs (DAGs) are naturally tailored to represent biological signalling pathways. However, a causal DAG is only identifiable up to Markov equivalence if only observational data are available. Interventional data, based on exogenous perturbations of the system, can greatly improve identifiability. Since the gain of an intervention crucially depends on the intervened variables

Quantifying the association between discrete event time series with applications to digital forensics J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200127
Christopher Galbraith; Padhraic Smyth; Hal S. SternWe consider the problem of quantifying the degree of association between pairs of discrete event time series, with potential applications in forensic and cybersecurity settings. We focus in particular on the case where two associated event series exhibit temporal clustering such that the occurrence of one type of event at a particular time increases the likelihood that an event of the other type will

A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200123
Maria Cuellar; Edward H. KennedyCurrent estimation methods for the probability of causation ‘PC’ make strong parametric assumptions or are inefficient. We derive a non‐parametric influence‐function‐based estimator for a projection of PC, which allows for simple interpretation and valid inference by making weak structural assumptions. We apply our estimator to real data from an experiment in Kenya. This experiment found, by estimating

Health effects of power plant emissions through ambient air quality J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200122
Chanmin Kim; Lucas R. F. Henneman; Christine Choirat; Corwin M. ZiglerCoal burning power plants are a frequent target of regulatory programmes because of their emission of chemicals that are known precursors to the formation of ambient particulate air pollution. Health impact assessments of emissions from coal power plants typically rely on assumed causal relationships between emissions, ambient pollution and health, many of which have never been empirically verified

On quantifying expert opinion about multinomial models that contain covariates J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200108
Fadlalla G. Elfadaly; Paul H. GarthwaiteThe paper addresses the task of forming a prior distribution to represent expert opinion about a multinomial model that contains covariates. The task has not previously been addressed. We suppose that the sampling model is a multinomial logistic regression and represent expert opinion about the regression coefficients by a multivariate normal distribution. This logistic–normal model gives a flexible

Knowing the signs: a direct and generalizable motivation of two‐sided tests J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191226
Kenneth Rice; Tyler Bonnett; Chloe KrakauerMany well‐known problems with two‐sided p‐values are due to their use in hypothesis tests, with ‘reject–accept’ conclusions about point null hypotheses. We present an alternative motivation for p‐value‐based tests, viewing them as assessments of only the sign of an underlying parameter, where we can conclude that the parameter is positive or negative, or simply say nothing either way. Our approach

A new standard for the analysis and design of replication studies J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191226
Leonhard HeldA new standard is proposed for the evidential assessment of replication studies. The approach combines a specific reverse Bayes technique with prior‐predictive tail probabilities to define replication success. The method gives rise to a quantitative measure for replication success, called the sceptical p‐value. The sceptical p‐value integrates traditional significance of both the original and the replication

A quantitative framework to inform extrapolation decisions in children J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191212
Ian Wadsworth; Lisa V. Hampson; Thomas Jaki; Graeme J. Sills; Anthony G. Marson; Richard AppletonWhen developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognized. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that of similar exposure–response (E–R‐) relationships. Motivated by applications to antiepileptic drug development, we consider

Crime against women in India: unveiling spatial patterns and temporal trends of dowry deaths in the districts of Uttar Pradesh J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191212
G. Vicente; T. Goicoa; P. Fernandez‐Rasines; M. D. UgarteCrimes against women in India have been continuously increasing lately as reported by the National Crime Records Bureau. Gender‐based violence has become a serious issue to such an extent that it has been catalogued as a high impact health problem by the World Health Organization. However, there is a lack of spatiotemporal analyses to reveal a complete picture of the geographical and temporal patterns

A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191125
K. Shuvo Bakar; Nicholas Biddle; Philip Kokic; Huidong JinMotivated by the Australian National University poll, we consider a situation where survey data have been collected from respondents for several categorical variables and a primary geographic classification, e.g. postcode. Here, a common and important problem is to obtain estimates for a second target geography that overlaps with the primary geography but has not been collected from the respondents

Using hidden information and performance level boundaries to study student–teacher assignments: implications for estimating teacher causal effects J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191124
J. R. Lockwood; D. McCaffreyA common problem in educational evaluation is estimating causal effects of interventions from non‐experimental data on students. Scores from standardized achievement tests often are used to adjust for differences in background characteristics of students in different non‐experimental groups. An open question is whether, and how, these adjustments should account for the errors in test scores as measures

Can genetics reveal the causes and consequences of educational attainment? J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191124
Marcus Munafò; Neil M. Davies; George Davey SmithThere is an extensive literature on the causes of educational inequalities, and the life course consequences of educational attainment. Mendelian randomization, where genetic variants associated with exposures of interest are used as proxies for those exposures, often within an instrumental variables framework, has proven highly effective at elucidating the causal effects of several risk factors in

Inferring the outcomes of rejected loans: an application of semisupervised clustering J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191114
Zhiyong Li; Xinyi Hu; Ke Li; Fanyin Zhou; Feng ShenRejection inference aims to reduce sample bias and to improve model performance in credit scoring. We propose a semisupervised clustering approach as a new rejection inference technique. K‐prototype clustering can deal with mixed types of numeric and categorical characteristics, which are common in consumer credit data. We identify homogeneous acceptances and rejections and assign labels to part of

Identification and sensitivity analysis of contagion effects in randomized placebo‐controlled trials J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191112
Kosuke Imai; Zhichao JiangIn social science research, interference between units is the rule rather than the exception. Contagion represents one key causal mechanism of such spillover effects, where one's treatment affects the outcome of another individual indirectly by changing the treated unit's own outcome. Alternatively, the treatment of one individual can affect the outcome of another person through other mechanisms. We

The hot hand in professional darts J. R. Stat. Soc. A (IF 2.21) Pub Date : 20191107
Marius Ötting; Roland Langrock; Christian Deutscher; Vianey Leos‐BarajasWe investigate the hot hand hypothesis in professional darts in a nearly ideal setting with minimal to no interaction between players. Considering almost 1 year of tournament data, corresponding to 167492 dart throws in total, we use state space models to investigate serial dependence in throwing performance. In our models, a latent state process serves as a proxy for a player's underlying form, and

Estimating the changing nature of Scotland's health inequalities by using a multivariate spatiotemporal model. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20190621
Eilidh Jack,Duncan Lee,Nema DeanHealth inequalities are the unfair and avoidable differences in people's health between different social groups. These inequalities have a huge influence on people's lives, particularly those who live at the poorer end of the socioeconomic spectrum, as they result in prolonged ill health and shorter lives. Most studies estimate health inequalities for a single disease, but this will give an incomplete

Bayesian joint modelling of longitudinal data on abstinence, frequency and intensity of drinking in alcoholism trials. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20190528
Eugenia Buta,Stephanie S O'Malley,Ralitza GueorguievaIn alcoholism research, several complementary outcomes are of interest:abstinence from drinking during a specific time frame, and, when the individual is drinking, frequency of drinking (the proportion of days on which drinking occurs) and intensity of drinking (the average number of drinks per drinking day). The outcomes are often measured repeatedly over time on the same subject and, although they

Informationanchored sensitivity analysis: theory and application. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20190305
Suzie Cro,James R Carpenter,Michael G KenwardAnalysis of longitudinal randomized clinical trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we are typically required to make an untestable assumption about postdeviation behaviour to perform our primary analysis and to estimate the treatment effect. In such settings, it is now widely recognized that we should follow

Discussion on From Start to Finish: a Framework for the Production of Small Area Official Statistics. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20190129
Seongho Kim,Weng Kee Wong 
Generalizing Evidence from Randomized Trials using Inverse Probability of Sampling Weights. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20181218
Ashley L Buchanan,Michael G Hudgens,Stephen R Cole,Katie R Mollan,Paul E Sax,Eric S Daar,Adaora A Adimora,Joseph J Eron,Michael J MugaveroResults obtained in randomized trials may not easily generalize to target populations. Whereas in randomized trials the treatment assignment mechanism is known, the sampling mechanism by which individuals are selected to participate in the trial is typically not known and assuming random sampling from the target population is often dubious. We consider an inverse probability of sampling weighted (IPSW)

Sensitivity of treatment recommendations to bias in network metaanalysis. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20181120
David M Phillippo,Sofia Dias,A E Ades,Vanessa Didelez,Nicky J WeltonNetwork metaanalysis (NMA) pools evidence on multiple treatments to estimate relative treatment effects. Included studies are typically assessed for risk of bias; however, this provides no indication of the impact of potential bias on a decision based on the NMA. We propose methods to derive bias adjustment thresholds which measure the smallest changes to the data that result in a change of treatment

The contributions of paradata and features of respondents, interviewers and survey agencies to panel cooperation in the Survey of Health, Ageing and Retirement in Europe. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20180717
Johanna Bristle,Martina Celidoni,Chiara Dal Bianco,Guglielmo WeberThis paper deals with panel cooperation in a crossnational, fully harmonized facetoface survey. Our outcome of interest is panel cooperation in the fourth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE). Following a multilevel approach, we focus on the contribution of paradata at three different levels: fieldwork strategies at the survey agency level, features of the (current)

Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20180616
Jingyu Liu,Walter W Piegorsch,A Grant Schissler,Susan L CutterWe develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a placebased vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial

Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20180612
Joshua L Warren,Penny GordonLarsenWhile there is a literature on the distribution of food stores across geographic and social space, much of this research uses crosssectional data. Analyses attempting to understand whether the availability of stores across neighborhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others.

Estimating onset time from longitudinal and crosssectional data with an application to estimating gestational age from longitudinal maternal anthropometry during pregnancy and neonatal anthropometry at birth. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20180605
Ana Maria OrtegaVilla,Katherine L Grantz,Paul S AlbertDetermining the date of conception is important for estimating gestational age and monitoring whether the fetus and mother are on track in their development and pregnancy. Various methods based on ultrasound have been proposed for dating a pregnancy in high resource countries. However, such techniques may not be available in underresourced countries. We develop a shared random parameter model for

Methods for estimating complier average causal effects for costeffectiveness analysis. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20180123
K DiazOrdaz,A J Franchini,R GrieveIn randomized controlled trials with treatment noncompliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approaches to costeffectiveness analyses, where methods need to recognize the correlation between cost and health outcomes. We propose a Bayesian full likelihood approach, which jointly models the effects of random assignment on treatment

Spatiotemporal trends in teen birth rates in the USA, 20032012. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20170613
Diba Khan,Lauren M Rossen,Brady Hamilton,Erin Dienes,Yulei He,Rong WeiThe objective of this analysis was to explore temporal and spatial variation in teen birth rates TBRs across counties in the USA, from 2003 to 2012, by using hierarchical Bayesian models. Prior examination of spatiotemporal variation in TBRs has been limited by the reliance on largescale geographies such as states, because of the potential instability in TBRs at smaller geographical scales such as

Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using treebased approaches: applications to fetal growth. J. R. Stat. Soc. A (IF 2.21) Pub Date : 20170228
Jared C Foster,Danping Liu,Paul S Albert,Aiyi LiuLongitudinal monitoring of biomarkers is often helpful for predicting disease or a poor clinical outcome. In this paper, We consider the prediction of both large and smallforgestationalage births using longitudinal ultrasound measurements, and attempt to identify subgroups of women for whom prediction is more (or less) accurate, should they exist. We propose a treebased approach to identifying