Siyu Heng, Bo Zhang, Xu Han, Scott A. Lorch, Dylan S. Small
Abstract Instrumental variables (IVs) are extensively used to handle unmeasured confounding. However, weak IVs may cause problems. Many matched studies have considered strengthening an IV through discarding some of the sample. It is widely accepted that strengthening an IV tends to increase the power of non-parametric tests and sensitivity analyses. We re-evaluate this conventional wisdom and offer new insights. First, we evaluate the trade-off between IV strength and sample size assuming a valid IV and exhibit conditions under which strengthening an IV increases power. Second, we derive a criterion for checking the validity of a sensitivity analysis model with a continuous dose and show that the widely used Γ sensitivity analysis model, which was used to argue that strengthening an IV increases the power of sensitivity analyses in large samples, does not work for continuous IVs. Third, we quantify the bias of the Wald estimator with a possibly invalid IV and leverage it to develop a valid sensitivity analysis framework and show that strengthening an IV may or may not increase the power of sensitivity analyses. We use our framework to study the effect on premature babies of being delivered in a high technology/high volume neonatal intensive care unit.
{"title":"Instrumental variables: to strengthen or not to strengthen?","authors":"Siyu Heng, Bo Zhang, Xu Han, Scott A. Lorch, Dylan S. Small","doi":"10.1093/jrsssa/qnad075","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad075","url":null,"abstract":"Abstract Instrumental variables (IVs) are extensively used to handle unmeasured confounding. However, weak IVs may cause problems. Many matched studies have considered strengthening an IV through discarding some of the sample. It is widely accepted that strengthening an IV tends to increase the power of non-parametric tests and sensitivity analyses. We re-evaluate this conventional wisdom and offer new insights. First, we evaluate the trade-off between IV strength and sample size assuming a valid IV and exhibit conditions under which strengthening an IV increases power. Second, we derive a criterion for checking the validity of a sensitivity analysis model with a continuous dose and show that the widely used Γ sensitivity analysis model, which was used to argue that strengthening an IV increases the power of sensitivity analyses in large samples, does not work for continuous IVs. Third, we quantify the bias of the Wald estimator with a possibly invalid IV and leverage it to develop a valid sensitivity analysis framework and show that strengthening an IV may or may not increase the power of sensitivity analyses. We use our framework to study the effect on premature babies of being delivered in a high technology/high volume neonatal intensive care unit.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135673191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Donald Michael Titterington, 1945–2023","authors":"Adrian Bowman","doi":"10.1093/jrsssa/qnad074","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad074","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135777169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Journal Article Memories of David Cox attending scientific talks Get access Vernon T Farewell Vernon T Farewell MRC Biostatistics Unit, University of Cambridge, Cambridge, UK Address for correspondence: Vernon T. Farewell, MRC Biostatistics Unit, School of Clinical Medicine, Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK. Email: vern.farewell@mrc-bsu.cam.ac.uk https://orcid.org/0000-0001-6704-5295 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad070, https://doi.org/10.1093/jrsssa/qnad070 Published: 30 May 2023 Article history Received: 28 March 2023 Accepted: 22 April 2023 Published: 30 May 2023
{"title":"Memories of David Cox attending scientific talks","authors":"Vernon T Farewell","doi":"10.1093/jrsssa/qnad070","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad070","url":null,"abstract":"Journal Article Memories of David Cox attending scientific talks Get access Vernon T Farewell Vernon T Farewell MRC Biostatistics Unit, University of Cambridge, Cambridge, UK Address for correspondence: Vernon T. Farewell, MRC Biostatistics Unit, School of Clinical Medicine, Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK. Email: vern.farewell@mrc-bsu.cam.ac.uk https://orcid.org/0000-0001-6704-5295 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad070, https://doi.org/10.1093/jrsssa/qnad070 Published: 30 May 2023 Article history Received: 28 March 2023 Accepted: 22 April 2023 Published: 30 May 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135643705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ian Diamond, Grant Fitzner, Richard Heys, Michael Keoghan, Darren Morgan
{"title":"The future of economic statistics","authors":"Ian Diamond, Grant Fitzner, Richard Heys, Michael Keoghan, Darren Morgan","doi":"10.1093/jrsssa/qnad072","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad072","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135643281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Assessing novel methods for increasing power system resilience against cyber-physical hazards requires real power grid data or high-quality synthetic data. However, for security reasons, even basic connection information for real power grid data are not publicly available. We develop a randomised model for generating realistic synthetic power networks based on the Delaunay triangulation and demonstrate that it captures important features of real power networks. To validate our model, we introduce a new metric for network similarity based on topological data analysis. We demonstrate the utility of our approach in application to IEEE test cases and European power networks. We identify the model parameters for two IEEE test cases and two European power grid networks and compare the properties of the generated networks with their corresponding benchmark networks.
{"title":"From Delaunay triangulation to topological data analysis: generation of more realistic synthetic power grid networks","authors":"Asim K Dey, Stephen J Young, Yulia R Gel","doi":"10.1093/jrsssa/qnad066","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad066","url":null,"abstract":"Assessing novel methods for increasing power system resilience against cyber-physical hazards requires real power grid data or high-quality synthetic data. However, for security reasons, even basic connection information for real power grid data are not publicly available. We develop a randomised model for generating realistic synthetic power networks based on the Delaunay triangulation and demonstrate that it captures important features of real power networks. To validate our model, we introduce a new metric for network similarity based on topological data analysis. We demonstrate the utility of our approach in application to IEEE test cases and European power networks. We identify the model parameters for two IEEE test cases and two European power grid networks and compare the properties of the generated networks with their corresponding benchmark networks.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135335835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Correction to: The Use of a Three-Level <i>M</i>-Quantile Model to Map Poverty at Local Administrative Unit 1 in Poland","authors":"","doi":"10.1093/jrsssa/qnad073","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad073","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135768137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Journal Article Medical Statistics for Cancer Studies Get access Medical Statistics for Cancer Studies by Cox Trevor F. June 23, 2022. 333 pp. $110.00. ISBN: 9781000601152 Amit K Chowdhry Amit K Chowdhry Wilmot Cancer Institute, University of Rochester, Rochester, USA amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad040, https://doi.org/10.1093/jrsssa/qnad040 Published: 12 April 2023
期刊文章医学统计癌症研究获取医学统计癌症研究由考克斯特雷弗F. 2022年6月23日。333页，110美元。ISBN: 9781000601152 Amit K Chowdhry Amit K Chowdhry Wilmot癌症研究所，罗切斯特大学，罗切斯特，美国amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060搜索作者的其他作品:牛津学术谷歌皇家统计学会学者杂志系列A:社会统计，qnad040, https://doi.org/10.1093/jrsssa/qnad040出版:2023年4月12日
{"title":"Medical Statistics for Cancer Studies","authors":"Amit K Chowdhry","doi":"10.1093/jrsssa/qnad040","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad040","url":null,"abstract":"Journal Article Medical Statistics for Cancer Studies Get access Medical Statistics for Cancer Studies by Cox Trevor F. June 23, 2022. 333 pp. $110.00. ISBN: 9781000601152 Amit K Chowdhry Amit K Chowdhry Wilmot Cancer Institute, University of Rochester, Rochester, USA amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad040, https://doi.org/10.1093/jrsssa/qnad040 Published: 12 April 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134952069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Journal Article Principles of Biostatistics Get access Principles of Biostatistics by Pagano Marcello, Gauvreau Kimberlee, Mattie Heather. June 7, 2022. 620 pp. $110.00. ISBN: 9780367355807 Amit K Chowdhry Amit K Chowdhry Wilmot Cancer Institute, University of Rochester, Rochester, USA amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad038, https://doi.org/10.1093/jrsssa/qnad038 Published: 12 April 2023
获取Pagano Marcello, gauveau Kimberlee, Mattie Heather的《生物统计学原理》。2022年6月7日。620页，110美元。ISBN: 9780367355807 Amit K Chowdhry Amit K Chowdhry Wilmot癌症研究所，罗切斯特大学，罗切斯特，美国amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060搜索作者的其他作品:牛津学术谷歌学者杂志皇家统计学会系列A:社会统计，qnad038, https://doi.org/10.1093/jrsssa/qnad038出版:2023年4月12日
{"title":"Principles of Biostatistics","authors":"Amit K Chowdhry","doi":"10.1093/jrsssa/qnad038","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad038","url":null,"abstract":"Journal Article Principles of Biostatistics Get access Principles of Biostatistics by Pagano Marcello, Gauvreau Kimberlee, Mattie Heather. June 7, 2022. 620 pp. $110.00. ISBN: 9780367355807 Amit K Chowdhry Amit K Chowdhry Wilmot Cancer Institute, University of Rochester, Rochester, USA amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad038, https://doi.org/10.1093/jrsssa/qnad038 Published: 12 April 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135289414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The topic of neural networks has seen a surge of interest in recent years. However, one of the main challenges with these approaches is quantification of uncertainty. The use of random weight models offer a potential solution. In addition to uncertainty quantification, these models are extremely computationally efficient as they do not require optimisation through stochastic gradient descent. We show how this approach can be used to account for informative sampling of survey data through the use of a pseudo-likelihood. We illustrate the effectiveness of this methodology through simulation and data application involving American National Election Studies data.
{"title":"Computationally efficient Bayesian unit-level random neural network modelling of survey data under informative sampling for small area estimation","authors":"Paul A Parker, Scott H Holan","doi":"10.1093/jrsssa/qnad033","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad033","url":null,"abstract":"Abstract The topic of neural networks has seen a surge of interest in recent years. However, one of the main challenges with these approaches is quantification of uncertainty. The use of random weight models offer a potential solution. In addition to uncertainty quantification, these models are extremely computationally efficient as they do not require optimisation through stochastic gradient descent. We show how this approach can be used to account for informative sampling of survey data through the use of a pseudo-likelihood. We illustrate the effectiveness of this methodology through simulation and data application involving American National Election Studies data.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136194563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependence and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the challenge in modelling and inferring correlation between a large number of variables.
{"title":"Networks with correlated edge processes","authors":"Maria Süveges, Sofia Charlotta Olhede","doi":"10.1093/jrsssa/qnad028","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad028","url":null,"abstract":"Abstract This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependence and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the challenge in modelling and inferring correlation between a large number of variables.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136174588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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