Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment

被引:0
作者
Marjan Meurisse
Francisco Estupiñán-Romero
Javier González-Galindo
Natalia Martínez-Lizaga
Santiago Royo-Sierra
Simon Saldner
Lorenz Dolanski-Aghamanoukjan
Alexander Degelsegger-Marquez
Stian Soiland-Reyes
Nina Van Goethem
Enrique Bernal-Delgado
机构
[1] Department of Epidemiology and Public Health,IREC – EPID
[2] Université Catholique de Louvain,Data science for Health Services and Policy
[3] Instituto Aragonés de Ciencias de la Salud (IACS),Data Archiving and Networked Services
[4] Royal Netherlands Academy of Arts & Sciences,International Affairs, Policy, Evaluation and Digitalisation
[5] Gesundheit Österreich GmbH (GÖG),Department of Computer Science
[6] The University of Manchester,Informatics Institute
[7] Universiteit van Amsterdam,undefined
来源
BMC Medical Research Methodology | / 23卷
关键词
Federated analysis; Causal inference; Real-world data; Comparative effectiveness; Vaccines; COVID-19; Pandemic preparedness;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 290 条
[1]  
Greenland S(2009)Identifiability, exchangeability and confounding revisited Epidemiol Perspect Innov 6 409-415
[2]  
Robins JM(2016)Causal inference from observational data Commun Dent Oral Epidemiol 44 1895-1903
[3]  
Listl S(2016)Causal inference—so much more than statistics Int J Epidemiol 45 615-625
[4]  
Jürges H(2004)A structural approach to selection bias Epidemiology 15 2446-2447
[5]  
Watt RG(2022)Target trial emulation: a framework for causal inference from observational data JAMA 328 578-586
[6]  
Pearce N(2006)Estimating causal effects from epidemiological data J Epidemiol Community Health 60 61-75
[7]  
Lawlor DA(2013)Causal inference in public health Annu Rev Public Health 34 221-1382
[8]  
Hernán MA(2021)Coping with interoperability in the development of a federated research infrastructure: achievements, challenges and recommendations from the JA-InfAct Arch Public Health 79 1372-1944
[9]  
Hernández-Díaz S(2010)DataSHIELD: resolving a conflict in contemporary bioscience—performing a pooled analysis of individual-level data without sharing the data Int J Epidemiol 39 1929-107
[10]  
Robins JM(2014)DataSHIELD: taking the analysis to the data, not the data to the analysis Int J Epidemiol 43 96-877