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Personalised need of care in an ageing society: The making of a prediction tool based on register data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210116
Marvin N. Wright; Sasmita Kusumastuti; Laust H. Mortensen; Rudi G. J. Westendorp; Thomas A. GerdsDanish municipalities monitor older persons who are at high risk of declining health and would later need home care services. However, there is no established strategy yet on how to accurately identify those who are at high risk. Therefore, there is great potential to optimise the municipalities’ prevention strategies. Denmark’s comprehensive set of electronic population registers provide longitudinal

Nowcasting monthly GDP with big data: A model averaging approach J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210112
Tommaso Proietti; Alessandro GiovannelliGross domestic product (GDP) is the most comprehensive and authoritative measure of economic activity. The macroeconomic literature has focused on nowcasting and forecasting this measure at the monthly frequency, using related high‐frequency indicators. We address the issue of estimating monthly GDP using a large‐dimensional set of monthly indicators, by pooling the disaggregate estimates arising from

Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210109
Olanrewaju Akande; Gabriel Madson; D. Sunshine Hillygus; Jerome P. ReiterOften, government agencies and survey organizations know the population counts or percentages for some of the variables in a survey. These may be available from auxiliary sources, for example administrative databases or other high‐quality surveys. We present and illustrate a model‐based framework for leveraging such auxiliary marginal information when handling unit and item nonresponse. We show how

Exploiting new forms of data to study the private rented sector: Strengths and limitations of a database of rental listings J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210109
Mark Livingston; Francesca Pannullo; Adrian W. Bowman; E. Marian Scott; Nick BaileyReviews of official statistics for UK housing have noted that developments have not kept pace with real‐world change, particularly the rapid growth of private renting. This paper examines the potential value of big data in this context. We report on the construction of a dataset from the on‐line adverts of one national lettings agency, describing the content of the dataset and efforts to validate it

Functional ANOVA modelling of pedestrian counts on streets in three European cities J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210109
David Bolin; Vilhelm Verendel; Meta Berghauser Pont; Ioanna Stavroulaki; Oscar Ivarsson; Erik HåkanssonThe relation between pedestrian flows, the structure of the city and the street network is of central interest in urban research. However, studies of this have traditionally been based on small data sets and simplistic statistical methods. Because of a recent large‐scale cross‐country pedestrian survey, there is now enough data available to study this in greater detail than before, using modern statistical

Sample size determination for risk‐based tax auditing J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210105
Petros Dellaportas; Evangelos Ioannidis; Christos KotsogiannisA modern system of Revenue Administration requires an effective and efficient management of compliance which in turn requires a well‐designed taxpayers audit strategy. The selection of taxpayers to be audited by Revenue Authorities is a non‐standard sample size determination problem, involving an initial random sample from the population and, based on the statistical information derived from it, a

Consistent aggregation with superlative and other price indices J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201205
Ludwig von Auer; Jochen WengenrothVarious fields of economic analysis (e.g. growth and productivity) and economic policy (e.g. monetary and social policy) rely on accurate measures of price change. Unfortunately, the price index formulae that most price statisticians consider as particularly accurate—the superlative indices of Fisher, Törnqvist, and Walsh—are believed to violate the property of consistency in aggregation. This property

Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201125
Li‐Chun ZhangPurchase data from retail chains can provide proxy measures of private household expenditure on items that are the most troublesome to collect in the traditional expenditure survey. Due to the inevitable coverage and selection errors, bias must exist in these proxy measures. Moreover, given the sheer amount of data, the bias completely dominates the variance. To investigate the potential of replacing

A seasonal dynamic measurement model for summer learning loss J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201123
Daniel McNeish; Denis DumasResearch conducted in US schools shows summer learning loss in test scores. If this summer loss is not incorporated into models of student ability growth, assumptions will be violated because fall scores will be overestimated and spring scores will be underestimated, which can be particularly problematic when evaluating teacher or school effectiveness. Statistical methods for summer loss have remained

On probability distributions of the time deviation law of container liner ships under interference uncertainty J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201117
Yunting Song; Nuo WangContainer liner shipping is a kind of transportation mode that is operated according to a schedule. Although the goal is to operate container liner ships on time, the actual arrival time and handling time often deviate from the schedule due to uncertain factors. The identification of a proper probability distribution to describe time deviation law will have a significant impact on accurately recognizing

Specification and testing of hierarchical ordered response models with anchoring vignettes J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201112
William H. Greene; Mark N. Harris; Rachel J. Knott; Nigel RiceCollection and analysis of self‐reported information on an ordered Likert scale is ubiquitous across the social sciences. Inference from such analyses is valid where the response scale employed means the same thing to all individuals. That is, if there is no differential item functioning (DIF) present in the data. A priori this is unlikely to hold across all individuals and cohorts in any sample of

A double machine learning approach to estimate the effects of musical practice on student’s skills J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201112
Michael C. KnausThis study investigates the dose–response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning

Linkage‐data linear regression J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201111
Li‐Chun Zhang; Tiziana TuotoData linkage is increasingly being used to combine data from different sources with the aim of identifying and bringing together records from separate files, which correspond to the same entities. Usually, data linkage is not a trivial procedure and linkage errors, false and missed links, are unavoidable. In these cases, standard statistical techniques may produce misleading inference. In this paper

Synthetic microdata for establishment surveys under informative sampling J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201110
Hang J. Kim; Jörg Drechsler; Katherine J. ThompsonMany agencies are investigating whether releasing synthetic microdata could be a viable dissemination strategy for highly sensitive data, such as business data, for which disclosure avoidance regulations otherwise prohibit the release of public use microdata. However, existing methods assume that the original data either cover the entire population or comprise a simple random sample, which limits the

Estimating causal moderation effects with randomized treatments and non‐randomized moderators J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201110
Kirk BansakResearchers are often interested in analysing conditional treatment effects. One variant of this is ‘causal moderation’, which implies that intervention upon a third (moderator) variable would alter the treatment effect. This study considers the conditions under which causal moderation can be identified and presents a generalized framework for estimating causal moderation effects given randomized treatments

A dynamic factor model approach to incorporate Big Data in state space models for official statistics J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201109
Caterina Schiavoni; Franz Palm; Stephan Smeekes; Jan van den BrakelIn this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high‐dimensional data sources. We apply the methodology to unemployment estimation as done by Statistics Netherlands, who uses a multivariate state space model to produce monthly figures for unemployment using series observed with the labour force

Beyond generalization of the ATE: Designing randomized trials to understand treatment effect heterogeneity J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Elizabeth TiptonResearchers conducting randomized trials have increasingly shifted focus from the average treatment effect to understanding moderators of treatment effects. Current methods for exploring moderation focus on model selection and hypothesis tests. At the same time, recent developments in the design of randomized trials have argued for the need for population‐based recruitment in order to generalize well

A dynamic separable network model with actor heterogeneity: An application to global weapons transfers* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Michael Lebacher; Paul W. Thurner; Göran KauermannIn this paper, we analyse the network of international major conventional weapons (MCW) transfers from 1950 to 2016, based on data from the Stockholm International Peace Research Institute (SIPRI). The dataset consists of yearly bilateral arms transfers between pairs of countries, which allows us to conceive of the individual relationships as part of an overall trade network. For the analysis, we extend

Ranking, and other properties, of elite swimmers using extreme value theory J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Harry Spearing; Jonathan Tawn; David Irons; Tim Paulden; Grace BennettThe International Swimming Federation (FINA) uses a very simple points system with the aim to rank swimmers across all swimming events. The points acquired is a function of the ratio of the recorded time and the current world record for that event. With some world records considered ‘better’ than others however, bias is introduced between events, with some being much harder to attain points where the

Interviewer effects and the measurement of financial literacy J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Thomas F. Crossley; Tobias Schmidt; Panagiota Tzamourani; Joachim K. WinterIn this paper, we ask whether interviewers influence the answers to a standard set of survey questions on financial literacy. We study data from Germany's wealth survey, the Panel on Household Finances (PHF). We have access to extensive auxiliary data, including interviewer identifiers, background characteristics of interviewers and measures of interviewer activity through the survey. We find that

Do coefficients of variation of response propensities approximate non‐response biases during survey data collection? J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201103
Jamie C. Moore; Gabriele B. Durrant; Peter W. F. SmithWe evaluate the utility of coefficients of variation of response propensities (CVs) as measures of risks of survey variable non‐response biases when monitoring survey data collection. CVs quantify variation in sample response propensities estimated given a set of auxiliary attribute covariates observed for all subjects. If auxiliary covariates and survey variables are correlated, low levels of propensity

Quantifying longevity gaps using micro‐level lifetime data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201103
Frank van Berkum; Katrien Antonio; Michel VellekoopUsing flexible Poisson regressions, we analyse a huge micro‐level lifetime dataset from a Dutch pension fund, including categorical, continuous and spatial risk factors collected on participants in the fund. The availability of granular lifetime data allows us to quantify the longevity gap between the national population and the fund on the one hand, and between participants within the fund on the

A Bayesian structural time series analysis of the effect of basic income on crime: Evidence from the Alaska Permanent Fund* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201103
Richard DorsettThis paper examines the impact of Alaska’s Permanent Fund Dividend on crime. The Dividend has been payable annually to state residents since 1982 and is the world’s longest‐running example of a basic income. Initially universal, from 1989 onwards eligibility was withdrawn from an increasing proportion of those in prison. A Bayesian structural time series estimator is used to simulate Alaskan crime

The effects of health on the extensive and intensive margins of labour supply J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201102
Lixin CaiUsing the first seven waves of the Understanding Society data, this study estimates the effects of health on the extensive and intensive margins of the labour supply of the UK workers. Earlier studies on the effects of health on labour supply tend to focus on a binary measure of labour force participation or early retirement. The results show that health affects both the margins of labour supply for

Did you conduct a sensitivity analysis? A new weighting‐based approach for evaluations of the average treatment effect for the treated J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201102
Guanglei Hong; Fan Yang; Xu QinIn non‐experimental research, a sensitivity analysis helps determine whether a causal conclusion could be easily reversed in the presence of hidden bias. A new approach to sensitivity analysis on the basis of weighting extends and supplements propensity score weighting methods for identifying the average treatment effect for the treated (ATT). In its essence, the discrepancy between a new weight that

Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201101
Bent Jesper Christensen; Nabanita Datta Gupta; Paolo Santucci de MagistrisUsing annual data from 1978 through 2016, and monthly data from January 2005 through November 2017 from Denmark, we provide a precise estimate of the upper bound on the potential impact of the adoption of wind energy on the reduction of CO 2 emissions from energy production. We separate causal impacts from endogenous effects in regressions using instrumental variables including average wind speed,

Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201101
Kamaryn T. Tanner; Linda D. Sharples; Rhian M. Daniel; Ruth H. KeoghDynamic prediction models provide predicted survival probabilities that can be updated over time for an individual as new measurements become available. Two techniques for dynamic survival prediction with longitudinal data dominate the statistical literature: joint modelling and landmarking. There is substantial interest in the use of machine learning methods for prediction; however, their use in the

Flexible instrumental variable distributional regression J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200816
Guillermo Briseño Sanchez; Maike Hohberg; Andreas Groll; Thomas KneibWe tackle two limitations of standard instrumental variable regression in experimental and observational studies: restricted estimation to the conditional mean of the outcome and the assumption of a linear relationship between regressors and outcome. More flexible regression approaches that solve these limitations have already been developed but have not yet been adopted in causality analysis. The

Bayesian econometric modelling of observational data for cost‐effectiveness analysis: establishing the value of negative pressure wound therapy in the healing of open surgical wounds J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200805
Pedro Saramago; Karl Claxton; Nicky J. Welton; Marta SoaresIn the absence of evidence from randomized controlled trials on the relative effectiveness of treatments, cost‐effectiveness analyses increasingly use observational data instead. Treatment assignment is not, however, randomized, and naive estimates of the treatment effect may be biased. To deal with this bias, one may need to adjust for observed and unobserved confounders. In this work we explore and

Assessing causal effects of extra compulsory learning on college students’ academic performances J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200826
Federica Licari; Alessandra MatteiIn Italian state universities candidate freshmen must take an entrance examination. Candidates who obtain a test score less than or equal to a pre‐fixed threshold may enrol at the university but must comply with an additional compulsory educational obligation, called obblighi formativi aggiuntivi (OFA). The OFA assignment rule appeals to a (sharp) regression discontinuity design with the entrance examination

Is being an only child harmful to psychological health?: evidence from an instrumental variable analysis of China's one‐child policy J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200812
Shuxi Zeng; Fan Li; Peng DingThe paper evaluates the effects of being an only child in a family on psychological health, leveraging data on the one‐child policy in China. We use an instrumental variable approach to address the potential unmeasured confounding between the fertility decision and psychological health, where the instrumental variable is an index of the intensity of the implementation of the policy. We establish an

Christopher John Skinner, 1953–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201005
Ray Chambers; Ian Diamond; Tim Holt; Jouni Kuha; Danny Pfeffermann; Natalie Shlomo; Pedro Nascimento Silva; Paul Smith; David Steel; Fiona SteeleChris Skinner was born in Penge, South London, on March 12th, 1953, the elder son of Richard and Daphne Skinner. His father worked for Lloyds of London, and his mother worked at the family‐run furniture store, Edginton's, in Penge. He showed an early aptitude for mathematics at St Dunstan's College, Catford, gaining a scholarship to Trinity College, Cambridge, and graduating with first‐class Honours

Gerald Joseph Goodhardt, 1930–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201005
Chris ChatfieldGerald Goodhardt was a distinguished marketing scientist who was also a fine statistician. He was always determined to keep one foot firmly in both camps, and he was for example Chairman of the Market Research Society (1973–1974) as well as being Honorary Secretary of the Royal Statistical Society (RSS) (1982–1988). His research contributions were mainly in the area of consumer purchasing behaviour

Willem van Zwet, 1934–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201005
P. J. Bickel; N. I. FisherWillem van Zwet passed away on July 2nd, 2020, after a short illness. He was an outstanding researcher, teacher and educator in mathematical statistics and probability, the father of statistics in the Netherlands, and the leading figure in opening up dialogue with colleagues in eastern Europe. His lasting contributions to the profession through his research and his work with professional societies

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 metaregression for populationadjusted 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; Lewis 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

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

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

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