On the current and future potential of simulations based on directed acyclic graphs
Real-world data are playing an increasingly important role in regulatory decision making. Adequately addressing bias is of paramount importance in this context. Structural representations of bias using directed acyclic graphs (DAGs) provide a unified approach to conceptualize bias, distinguish betwe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Global Epidemiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590113325000045 |
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| author | Lutz P. Breitling Anca D. Dragomir Chongyang Duan George Luta |
| author_facet | Lutz P. Breitling Anca D. Dragomir Chongyang Duan George Luta |
| author_sort | Lutz P. Breitling |
| collection | DOAJ |
| description | Real-world data are playing an increasingly important role in regulatory decision making. Adequately addressing bias is of paramount importance in this context. Structural representations of bias using directed acyclic graphs (DAGs) provide a unified approach to conceptualize bias, distinguish between different types of bias, and identify ways to address bias. DAG-based data simulation further enhances the scope of this approach. Recently, DAGs have been used to demonstrate how missing eligibility information can compromise emulated target trial analysis, a cutting edge approach to estimate treatment effects using real-world data. The importance of simulation for methodological research has received substantial recognition in the past few years, and others have argued that simulating data based on DAGs can be especially helpful for understanding various epidemiological concepts. In the present work, we present two concrete examples of how simulations based on DAGs can be used to gain insights into issues commonly encountered in real-world analytics, i.e., regression modelling to address confounding bias, and the potential extent of selection bias. Increasing accessibility and extending the simulation algorithms of existing software to include longitudinal and time-to-event data are identified as priorities for further development. With such extensions, simulations based on DAGs would be an even more powerful tool to advance our understanding of the rapidly growing toolbox of real-world analytics. |
| format | Article |
| id | doaj-art-d6162dcf64a34ceab2ce548b1ca5ecf3 |
| institution | Kabale University |
| issn | 2590-1133 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Epidemiology |
| spelling | doaj-art-d6162dcf64a34ceab2ce548b1ca5ecf32025-01-26T05:04:45ZengElsevierGlobal Epidemiology2590-11332025-06-019100186On the current and future potential of simulations based on directed acyclic graphsLutz P. Breitling0Anca D. Dragomir1Chongyang Duan2George Luta3Medical Faculty, University of Heidelberg, Heidelberg, Germany; Corresponding author at: Ministry of Justice and Migration Baden-Württemberg, Department IV, Schillerplatz 4, 70173 Stuttgart, Germany.Department of Oncology, Georgetown University, Washington, DC, USA; Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Frederiksberg, DenmarkDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, ChinaParker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Frederiksberg, Denmark; Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC, USA; Department of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkReal-world data are playing an increasingly important role in regulatory decision making. Adequately addressing bias is of paramount importance in this context. Structural representations of bias using directed acyclic graphs (DAGs) provide a unified approach to conceptualize bias, distinguish between different types of bias, and identify ways to address bias. DAG-based data simulation further enhances the scope of this approach. Recently, DAGs have been used to demonstrate how missing eligibility information can compromise emulated target trial analysis, a cutting edge approach to estimate treatment effects using real-world data. The importance of simulation for methodological research has received substantial recognition in the past few years, and others have argued that simulating data based on DAGs can be especially helpful for understanding various epidemiological concepts. In the present work, we present two concrete examples of how simulations based on DAGs can be used to gain insights into issues commonly encountered in real-world analytics, i.e., regression modelling to address confounding bias, and the potential extent of selection bias. Increasing accessibility and extending the simulation algorithms of existing software to include longitudinal and time-to-event data are identified as priorities for further development. With such extensions, simulations based on DAGs would be an even more powerful tool to advance our understanding of the rapidly growing toolbox of real-world analytics.http://www.sciencedirect.com/science/article/pii/S2590113325000045Simulation studiesDirected acyclic graphsSelection biasConfoundingReal world evidenceRegression modelling |
| spellingShingle | Lutz P. Breitling Anca D. Dragomir Chongyang Duan George Luta On the current and future potential of simulations based on directed acyclic graphs Global Epidemiology Simulation studies Directed acyclic graphs Selection bias Confounding Real world evidence Regression modelling |
| title | On the current and future potential of simulations based on directed acyclic graphs |
| title_full | On the current and future potential of simulations based on directed acyclic graphs |
| title_fullStr | On the current and future potential of simulations based on directed acyclic graphs |
| title_full_unstemmed | On the current and future potential of simulations based on directed acyclic graphs |
| title_short | On the current and future potential of simulations based on directed acyclic graphs |
| title_sort | on the current and future potential of simulations based on directed acyclic graphs |
| topic | Simulation studies Directed acyclic graphs Selection bias Confounding Real world evidence Regression modelling |
| url | http://www.sciencedirect.com/science/article/pii/S2590113325000045 |
| work_keys_str_mv | AT lutzpbreitling onthecurrentandfuturepotentialofsimulationsbasedondirectedacyclicgraphs AT ancaddragomir onthecurrentandfuturepotentialofsimulationsbasedondirectedacyclicgraphs AT chongyangduan onthecurrentandfuturepotentialofsimulationsbasedondirectedacyclicgraphs AT georgeluta onthecurrentandfuturepotentialofsimulationsbasedondirectedacyclicgraphs |