Pub Date : 2023-01-01Epub Date: 2023-01-24DOI: 10.1093/jrsssa/qnac015
Christopher Jackson, Belen Zapata-Diomedi, James Woodcock
A widely-used model for determining the long-term health impacts of public health interventions, often called a "multistate lifetable", requires estimates of incidence, case fatality, and sometimes also remission rates, for multiple diseases by age and gender. Generally, direct data on both incidence and case fatality are not available in every disease and setting. For example, we may know population mortality and prevalence rather than case fatality and incidence. This paper presents Bayesian continuous-time multistate models for estimating transition rates between disease states based on incomplete data. This builds on previous methods by using a formal statistical model with transparent data-generating assumptions, while providing accessible software as an R package. Rates for people of different ages and areas can be related flexibly through splines or hierarchical models. Previous methods are also extended to allow age-specific trends through calendar time. The model is used to estimate case fatality for multiple diseases in the city regions of England, based on incidence, prevalence and mortality data from the Global Burden of Disease study. The estimates can be used to inform health impact models relating to those diseases and areas. Different assumptions about rates are compared, and we check the influence of different data sources.
一种广泛使用的确定公共卫生干预措施对健康的长期影响的模型通常被称为 "多州生命表"，它需要按年龄和性别估算多种疾病的发病率、病死率，有时还需要估算缓解率。一般来说，并非每种疾病和每种环境都有发病率和病死率的直接数据。例如，我们可能知道的是人口死亡率和患病率，而不是病例死亡率和发病率。本文提出了贝叶斯连续时间多状态模型，用于估计基于不完整数据的疾病状态之间的转换率。该模型建立在以往方法的基础上，使用了具有透明数据生成假设的正式统计模型，同时提供了易于使用的 R 软件包。不同年龄和地区人群的比率可以通过样条或层次模型灵活地联系起来。以前的方法也得到了扩展，允许通过日历时间呈现特定年龄的趋势。根据全球疾病负担研究的发病率、流行率和死亡率数据，该模型可用于估算英格兰城市地区多种疾病的病死率。估算结果可用于建立与这些疾病和地区相关的健康影响模型。我们对不同的发病率假设进行了比较，并检查了不同数据来源的影响。
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{"title":"Contents of volume 185, 2022","authors":"","doi":"10.1111/rssa.12994","DOIUrl":"https://doi.org/10.1111/rssa.12994","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S2","pages":"S781-S789"},"PeriodicalIF":2.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137729866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>I would like to thank Sylvia for a wonderful Presidential Address tonight. It feels like a different world from my own Presidential address delivered in the first year of my presidency in June 2019. In it, I concluded that the RSS for nearly 200 years has at its heart been about using data for the public good, while developing the statistical science and building the statistical capacity required to do that. My Presidential predecessors had identified many challenges that are still with us. In addition, I opined that we faced important new challenges. These include providing health and social care for people with increasing levels of multimorbidity, coupled with the pensions' crisis as people are living longer lives and also the effects of climate change as two areas where statisticians can make contributions. Those challenges have not gone away but, as Sylvia so eloquently describes, we had no idea of the scale of new challenge that was about to hit us.</p><p>Just over 6 months later, the world changed beyond recognition with the advent of SARS-CoV-2. Statisticians, along with many others, tried to get to grips and contribute to a huge range of issues in timelines and in a context that is unprecedented. As President, I was hugely grateful to Sylvia and David Spiegelhalter for agreeing to co-chair the RSS's Covid-19 Task Force, which co-ordinated the Society's response, as well as the group undertaking fearsome amounts of work themselves. Sylvia's magisterial Presidential address documents and reflects on the extraordinary work done under their leadership.</p><p>Building on some of Sylvia's themes, I would like to make some personal reflections on how the scientific community, including statisticians, achieved such a lot in such a short time, and to me, the key is a firm grounding of principles and preparedness, coupled with flexibility and agility. Sylvia mentions the REACT studies, and to declare my interests, I was an investigator in those. The reason the team was able to mount those studies so well and so quickly was combination of prior experience in large population studies in other clinical areas, combined with a huge logistical exercise between academic, government and private partners, the scale of which I only now fully appreciate. The pace of data coming in, reports being drafted, headlines feeding into policy arenas, then full reports being put in the public domain within days was in complete contrast to the painstaking ways epidemiologists traditionally work, but gave rise to a huge sense of satisfaction. The Covid19 Task Force generously played a pivotal role at the development stage of the REACT protocols, giving helpful critical feedback to improve the study designs by return that would normally take months through the normal academic grant-giving process.</p><p>In my own presidential address, I had presciently flagged up adaptive platform trials, describing them as the evolution of the Rothamsted ‘long-term experiments’. Sylvi
{"title":"Proposal of the vote of thanks for ‘Statistics in times of increasing uncertainty’, Sylvia Richardson's Presidential Address","authors":"Deborah Ashby","doi":"10.1111/rssa.12989","DOIUrl":"10.1111/rssa.12989","url":null,"abstract":"<p>I would like to thank Sylvia for a wonderful Presidential Address tonight. It feels like a different world from my own Presidential address delivered in the first year of my presidency in June 2019. In it, I concluded that the RSS for nearly 200 years has at its heart been about using data for the public good, while developing the statistical science and building the statistical capacity required to do that. My Presidential predecessors had identified many challenges that are still with us. In addition, I opined that we faced important new challenges. These include providing health and social care for people with increasing levels of multimorbidity, coupled with the pensions' crisis as people are living longer lives and also the effects of climate change as two areas where statisticians can make contributions. Those challenges have not gone away but, as Sylvia so eloquently describes, we had no idea of the scale of new challenge that was about to hit us.</p><p>Just over 6 months later, the world changed beyond recognition with the advent of SARS-CoV-2. Statisticians, along with many others, tried to get to grips and contribute to a huge range of issues in timelines and in a context that is unprecedented. As President, I was hugely grateful to Sylvia and David Spiegelhalter for agreeing to co-chair the RSS's Covid-19 Task Force, which co-ordinated the Society's response, as well as the group undertaking fearsome amounts of work themselves. Sylvia's magisterial Presidential address documents and reflects on the extraordinary work done under their leadership.</p><p>Building on some of Sylvia's themes, I would like to make some personal reflections on how the scientific community, including statisticians, achieved such a lot in such a short time, and to me, the key is a firm grounding of principles and preparedness, coupled with flexibility and agility. Sylvia mentions the REACT studies, and to declare my interests, I was an investigator in those. The reason the team was able to mount those studies so well and so quickly was combination of prior experience in large population studies in other clinical areas, combined with a huge logistical exercise between academic, government and private partners, the scale of which I only now fully appreciate. The pace of data coming in, reports being drafted, headlines feeding into policy arenas, then full reports being put in the public domain within days was in complete contrast to the painstaking ways epidemiologists traditionally work, but gave rise to a huge sense of satisfaction. The Covid19 Task Force generously played a pivotal role at the development stage of the REACT protocols, giving helpful critical feedback to improve the study designs by return that would normally take months through the normal academic grant-giving process.</p><p>In my own presidential address, I had presciently flagged up adaptive platform trials, describing them as the evolution of the Rothamsted ‘long-term experiments’. Sylvi","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 4","pages":"1497-1498"},"PeriodicalIF":2.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12989","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45961625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Referees","authors":"","doi":"10.1111/rssa.12969","DOIUrl":"https://doi.org/10.1111/rssa.12969","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 4","pages":"2310-2319"},"PeriodicalIF":2.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137555777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Discussion of Presidential address: Statistics in times of increasing uncertainty by Sylvia Richardson","authors":"David Spiegelhalter","doi":"10.1111/rssa.12970","DOIUrl":"10.1111/rssa.12970","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 4","pages":"1499-1500"},"PeriodicalIF":2.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48227375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Contents of volume 185, 2022","authors":"","doi":"10.1111/rssa.12990","DOIUrl":"10.1111/rssa.12990","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 4","pages":"2320-2325"},"PeriodicalIF":2.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45560770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The statistical community mobilised vigorously from the start of the 2020 SARS-CoV-2 pandemic, following the RSS's long tradition of offering our expertise to help society tackle important issues that require evidence-based decisions. This address aims to capture the highlights of our collective engagement in the pandemic, and the difficulties faced in delivering statistical design and analysis at pace and in communicating to the wider public the many complex issues that arose. I argue that these challenges gave impetus to fruitful new directions in the merging of statistical principles with constraints of agility, responsiveness and societal responsibilities. The lessons learned from this will strengthen the long-term impact of the discipline and of the Society. The need to evaluate policies even in emergency, and to strive for statistical interoperability in future disease surveillance systems is highlighted. In my final remarks, I look towards the future landscape for statistics in the fast-moving world of data science and outline a strategy of visible and growing engagement of the RSS with the data science ecosystem, building on the central position of statistics.
{"title":"Statistics in times of increasing uncertainty","authors":"Sylvia Richardson","doi":"10.1111/rssa.12957","DOIUrl":"10.1111/rssa.12957","url":null,"abstract":"<p>The statistical community mobilised vigorously from the start of the 2020 SARS-CoV-2 pandemic, following the RSS's long tradition of offering our expertise to help society tackle important issues that require evidence-based decisions. This address aims to capture the highlights of our collective engagement in the pandemic, and the difficulties faced in delivering statistical design and analysis at pace and in communicating to the wider public the many complex issues that arose. I argue that these challenges gave impetus to fruitful new directions in the merging of statistical principles with constraints of agility, responsiveness and societal responsibilities. The lessons learned from this will strengthen the long-term impact of the discipline and of the Society. The need to evaluate policies even in emergency, and to strive for statistical interoperability in future disease surveillance systems is highlighted. In my final remarks, I look towards the future landscape for statistics in the fast-moving world of data science and outline a strategy of visible and growing engagement of the RSS with the data science ecosystem, building on the central position of statistics.</p>","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 4","pages":"1471-1496"},"PeriodicalIF":2.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42500485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ian Reynold's discussion contribution to papers in Session 3 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11 June 2021","authors":"Ian Reynolds","doi":"10.1111/rssa.12983","DOIUrl":"10.1111/rssa.12983","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S152"},"PeriodicalIF":2.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45104905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Swapnil Mishra, James A. Scott, Daniel J. Laydon, Harrison Zhu, Neil M. Ferguson, Samir Bhatt, Seth Flaxman, Axel Gandy
We propose a new framework to model the COVID-19 epidemic of the United Kingdom at the local authority level. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic based on renewal equations, with some important innovations, including a random walk modelling the reproduction number, incorporating information from different sources, including surveys to estimate the time-varying proportion of infections that lead to reported cases or deaths, and modelling the underlying infections as latent random variables. The model is designed to be updated daily using publicly available data. We envisage the model to be useful for now-casting and short-term projections of the epidemic as well as estimating historical trends. The model fits are available on a public website: https://imperialcollegelondon.github.io/covid19local. The model is currently being used by the Scottish government to inform their interventions.
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I congratulate both teams for these welcome contributions on modelling the Covid-19 pandemic. To produce results of such quality within exacting timescales is a genuine achievement. Both studies infer a time-varying reproduction number R t from summary data by construct-ing hierarchical Bayesian frameworks embodying R t as an intrinsic parameter. Observations arise as noisy, time-shifted representations of an autoregressive infection process with weights specified by generation-time probabilities and moderated by R t . With a common root in Flaxman et al. (2020), the papers differ in their treatment of temporal effects and spatial cou-pling (with Teh et al. (2022) adopting an explicitly spatio-temporal Gaussian process for log R t while Mishra et al. (2022) use a random walk prior), in their use of data, and in underlying assumptions. Neither study, in the prior for R t , incorporates foreseeable effects such as step changes follow-ing interventions, the impact of improved testing on track-and-trace measures, or the expected decline in R t due to susceptible depletion. Incidentally, the presentation of the infection model in Mishra et al. (2022) seems confusing, with R t between equations (1) and (2) changing from an instantaneous reproduction number to a ‘raw’ reproduction number, subsequently re-scaled by the susceptible proportion before reporting. The papers’ general approach is arguably the ‘image analyst’s take’ on epidemic modelling, where the objective is to recover a ‘true’ R t from a noisy image, with prior distributions providing regularisation rather than capturing mechanistic thinking. This approach differs
我祝贺这两个团队在模拟Covid-19大流行方面做出的可喜贡献。在严格的时间尺度内产生如此高质量的结果是一项真正的成就。两项研究都通过构建层次贝叶斯框架，从汇总数据中推断出时变的再现数R t $$ {R}_t $$R t $$ {R}_t $$作为内在参数。观察结果是自回归感染过程的噪声时移表示，其权重由代时间概率指定，并由R t $$ {R}_t $$调节。与Flaxman等人(2020)的共同根源，这两篇论文在处理时间效应和空间耦合方面有所不同(Teh等人(2022)对log R t采用了明确的时空高斯过程$$ log {R}_t $$，而Mishra等人则采用了不同的方法。(2022)使用随机漫步先验)，在数据的使用和潜在的假设中。在R t $$ {R}_t $$之前的研究中，这两项研究都没有纳入可预见的影响，例如干预后的阶跃变化，改进测试对跟踪和跟踪措施的影响，或R t $$ {R}_t $$由于易感耗竭的预期下降。顺便提一下，Mishra等人(2022)对感染模型的描述似乎令人困惑，方程(1)和(2)之间的R t $$ {R}_t $$从瞬时繁殖数变为“原始”繁殖数，随后由报告前的敏感比例重新缩放。论文的一般方法可以说是“图像分析师对流行病建模的看法”，其目标是从噪声图像中恢复“真实”的R t $$ {R}_t $$，先验分布提供正则化，而不是捕获机械思维。这种方法不同于植物或动物病原体建模者经常采用的方法，后者旨在估计控制传播过程不同方面的参数，例如接触率和空间核函数，然后将“机制”理解外推到其他环境。R t $$ {R}_t $$这个可以定义的量是传播过程和假定的监测和控制策略的副产品，而不是一个内在参数。 ， 2019)?例如，当模拟具有繁殖矩阵R t $$ {mathbf{R}}_t $$的结构化种群的类似数据时，输入的R t $$ {R}_t $$能成功地跟踪真实R的最大特征值吗T $$ {mathbf{R}}_t $$，或者它可能低估了这个数量，因为组间的感染分布可能与相应的特征向量不匹配?与更简单的平滑方法进行比较也是受欢迎的。这两篇论文强调了流行病统计建模的一个重要挑战——对更复杂的机制模型的统计推断，这些模型可能为有针对性的控制策略的设计提供信息。这就要求可用数据的丰富程度与模型的复杂性更好地匹配;实现这样的匹配本身就是一个重大挑战。这些论文的作者有效地利用了现有数据，他们的建模是理解空间相互作用影响的重要一步。探索他们的框架是否延伸到其他异质性将是有趣的，例如年龄结构引起的异质性，其重要性在其他研究中已经得到强调(例如Lau等人，2020)。
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