首页期刊导航|Journal of the Royal Statistical Society, Series A. Statistics in society
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Journal of the Royal Statistical Society, Series A. Statistics in society
Blackwell Publishers Ltd.
Journal of the Royal Statistical Society, Series A. Statistics in society

Blackwell Publishers Ltd.

0964-1998

Journal of the Royal Statistical Society, Series A. Statistics in society/Journal Journal of the Royal Statistical Society, Series A. Statistics in society
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    Re-weighting the randomized controlled trial for generalization: finite-sample error and variable selection

    Benedicte ColnetJulie JosseGael VaroquauxErwan Scornet...
    345-372页
    查看更多>>摘要：Randomized controlled trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under-sample individuals with certain characteristics compared to the target population, for which one wants conclusions on treatment effectiveness. Re-weighting trial individuals to match the target population can improve the treatment effect estimation. In this work, we establish the expressions of the bias and variance of such re-weighting procedures-also called inverse propensity of sampling weighting (IPSW)-in presence of categorical covariates for any sample size. Such results allow us to compare the theoretical performance of different versions of IPSW estimates. Besides, our results show how the performance (bias, variance, and quadratic risk) of IPSW estimates depends on the two sample sizes (RCT and target population). A by-product of our work is the proof of consistency of IPSW estimates. In addition, we analyse how including covariates that are not necessary for identifiability of the causal effect may impact the asymptotic variance. Including covariates that are shifted between the two samples but not treatment-effect modifiers increases the variance while non-shifted but treatment-effect modifiers do not. We illustrate all the takeaways in a didactic example, and on a semi-synthetic simulation inspired from critical care medicine.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Probabilistic scenario-based assessment of national food security risks with application to Egypt and Ethiopia

    P. KoundouriG. I. PapayiannisA. VassilopoulosA. N. Yannacopoulos...
    373-409页
    查看更多>>摘要：This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socio-economic Pathways and Representative Concentration Pathways. This innovative method allows each scenario, encompassing socio-economic, and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Hierarchical multinomial processing tree models for meta-analysis of diagnostic accuracy studies

    Annamaria Guolo
    410-427页
    查看更多>>摘要：Meta-analysis represents a widely accepted approach for evaluating the accuracy of diagnostic tools in clinical and psychological investigations. This article investigates the applicability of multinomial tree models recently suggested in the literature under a fixed-effects formulation for assessing the accuracy of binary classification tools, where the study specific disease prevalences are taken into account. The model proposed in this article extends previous results to a hierarchical structure accounting for the variability between the studies included in the meta-analysis. Interestingly, by exploiting the parameter separability of the complete likelihood function, the resulting hierarchical multinomial tree model is shown to coincide, in its interest parameter component, with the well-known bivariate random-effects model under an exact within-study distribution for the number of true positives and true negatives subjects. The proposal is in line with a latent-trait approach, where inference follows a frequentist point of view. The applicability of the proposed model and its performance with respect to its fixed-effects counterpart and to the approximate bivariate random-effects model based on normality assumptions commonly used in the literature is evaluated in a series of simulation studies. Methods are applied to a real meta-analysis about the accuracy of the confusion assessment method as delirium screening tool.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Bayesian causal forests for multivariate outcomes: application to Irish data from an international large scale education assessment

    Nathan McJamesAnn O'SheaYong Chen GohAndrew Parnell...
    428-450页
    查看更多>>摘要：Bayesian Causal Forests (BCF) is a causal inference machine learning model based on the flexible non-parametric regression and classification tool, Bayesian Additive Regression Trees (BART). Motivated by data from the Trends in International Mathematics and Science Study (TIMSS), which includes data on student achievement in both mathematics and science, we present a multivariate extension of the BCF algorithm. With the help of simulation studies, we show that our approach can accurately estimate causal effects for multiple outcomes subject to the same treatment. We apply our model to Irish data from TIMSS 2019. Our findings reveal the positive effects of having access to a study desk at home (Mathematics ATE 95% Cl: [-0.50, 10.14]) while also highlighting the negative consequences of students often feeling hungry at school (Mathematics ATE 95% Cl: [-8.86, -1.56] , Science ATE 95% Cl: [-10.35, -0.94]) or often being absent (Mathematics ATE 95% Cl: [-11.88, -2.27]). Code for replicating the results can be found at https:// github.com/Nathan-McJames/MVBCF-Paper.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Effectiveness of nonpharmaceutical policy interventions in reducing population mobility during the COVID-19 pandemic

    Jonas KlingwortJoep BurgerJan van den Brakel
    451-473页
    查看更多>>摘要：Nonpharmaceutical policy interventions (NPIs) are intended to reduce population mobility in mitigating the spread of COVID-19. This paper evaluates their effect on population mobility during the COVID-19 pandemic. State space models are applied to estimate the time-varying effects of NPI stringency on weekly pedestrian counts from location-based sensors installed before the pandemic. Different models are developed that evaluate compliance with NPIs over time, identify the most effective NPI, and identify regional differences. An efficient parsimonious alternative is proposed for the multivariate Seemingly Unrelated Time Series Equation model if full covariance matrices are of full rank. Kalman filter estimates of the regression coefficients show that NPI stringency initially had a negative effect on population mobility. The effect weakened during the pandemic, suggesting a reduced willingness to comply with regulations. Four of nine NPIs were identified as the most effective. The multivariate model confirmed the findings across federal states. This paper highlights how combining new data sources, routinely collected administrative data, and sound methodology fosters modern policy evaluation.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Estimating the proportion of modern contraceptives supplied by the public and private sectors using a Bayesian hierarchical penalized spline model

    Hannah ComiskeyG, Leontine AlkemaNiamh Cahill
    474-490页
    查看更多>>摘要：Quantifying the public/private-sector supply of contraceptive methods within countries is vital for effective and sustainable family-planning delivery. However, many low-and middle-income countries quantify contraceptive supply using out-of-date Demographic Health Surveys. As an alternative, we propose using a Bayesian, hierarchical, penalized-spline model, with survey input, to produce annual estimates and projections of contraceptive supply-share outcomes. Our approach shares information across countries, accounts for survey observational errors and produces probabilistic projections informed by past changes in supply shares, as well as correlations between supply-share changes across different contraceptive methods. Results may be used to evaluate family-planning program effectiveness and stability.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Assessments and developments in constructing a National Health Index for policy-making, in the UK

    Anna Freni-SterrantinoThomas P. PrescottGreg CeelyMyer Glickman...
    491-514页
    查看更多>>摘要：Composite indicators are useful for summarizing and comparing changes among different communities. The UK Office for National Statistics has created an annual England Health Index (2015-2018) comprised of three main health domains-lives, places, and people-to monitor health over time and across different geographical areas and evaluate the nation's health. We reviewed the conceptual coherence and statistical requirements, focusing on three main steps: correlation analysis at different levels, comparison of the implemented weights, and a sensitivity and uncertainty analysis. Based on the results, we have highlighted features that have improved the statistical requirements of the forthcoming UK Health Index.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    A censored quantile transformation model for Alzheimer's Disease data with multiple functional covariates

    Shaopei MaMan-lai TangKerning YuWolfgang Karl Hardle...
    515-538页
    查看更多>>摘要：Alzheimer's disease (AD) is a progressive disease that starts from mild cognitive impairment and may eventually lead to irreversible memory loss. It is imperative to explore the risk factors associated with the conversion time to AD that is usually right-censored. Classical statistical models like mean regression and Cox models fail to quantify the impact of risk factors across different quantiles of a response distribution, and previous research has primarily focused on modelling a single functional covariate, possibly overlooking the interdependence among multiple functional covariates and other crucial features of the distribution. To address these issues, this paper proposes a multivariate functional censored quantile regression model based on dynamic power transformations, which relaxes the global linear assumption and provides more robustness and flexibility. Uniform consistency and weak convergence of the quantile process are established. Simulation studies suggest that the proposed method outperforms the existing approaches. Real data analysis shows the importance of both left and right hippocampal radial distance curves for predicting the conversion time to AD at different quantile levels.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Tracking trend output using expectations data

    Kevin LeeMichael Mahony
    539-565页
    查看更多>>摘要：This article proposes a new approach to measuring trend output that exploits survey data on expectations to distinguish the effects of permanent and transitory shocks and to track the time-variation in the processes underlying the determination of output. The approach is illustrated using measures of output expectations and output uncertainties based on a business survey conducted for UK manufacturing. The measures are employed in a time-varying vector autoregression (VAR) to track trend output and to provide a compelling characterization of the output fluctuations in UK manufacturing over the last 20 years.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Bias and excess variance in election polling: a not-so-hidden Markov model

    Graham TierneyAlexander Volfovsky
    566-582页
    查看更多>>摘要：With historic misses in the 2016 and 2020 US Presidential elections, interest in measuring polling errors has increased. The most common method for measuring directional errors and non-sampling excess variability during a postmortem for an election is by assessing the difference between the poll result and election result for polls conducted within a few days of the day of the election. Analysing such polling error data is notoriously difficult with typical models being extremely sensitive to the time between the poll and the election. We leverage hidden Markov models traditionally used for election forecasting to flexibly capture time-varying preferences and treat the election result as a peek at the typically hidden Markovian process. Our results are much less sensitive to the choice of time window, avoid conflating shifting preferences with polling error, and are more interpretable despite a highly flexible model. We demonstrate these results with data on polls from the 2004 through 2020 US Presidential elections and 1992 through 2020 US Senate elections, concluding that previously reported estimates of bias in Presidential elections were too extreme by 10%, estimated bias in Senatorial elections was too extreme by 25%, and excess variability estimates were also too large.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press