Causal diagrams in systems epidemiology

Emerging Themes in Epidemiology

Volumn 9, Issue , 2012, Pages

  Joffe, Michael ^a   Gambhir, Manoj ^a   Chadeau Hyam, Marc ^a   Vineis, Paolo ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Causation; Change models; DAGs; Diagrammatic methods; Epidemiological methodology; Feedback; Infectious disease epidemiology models; Instrumental variables; Web of causation

Indexed keywords

CAUSAL DIAGRAM; CHANGE MODEL; COMPARTMENT MODEL; CONCEPT FORMATION; DIRECTED ACYCLIC GRAPH; EPIDEMIOLOGY; FEEDBACK SYSTEM; HUMAN; INFECTION; INSTRUMENTAL VARIABLE ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PROGNOSIS; REVIEW; SOCIAL STATUS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 84858330031     PISSN: None     EISSN: 17427622     Source Type: Journal    
DOI: 10.1186/1742-7622-9-1     Document Type: Review

References (67)

1. Are statistical contributions to medicine undervalued? Breslow NE, Biometrics 2003 59 1 8 10.1111/1541-0420.00001 12762435 (Pubitemid 36523101)
2. Epstein P, Changing planet, changing health University of California Press 2011
3. Pearl J, Causality: models, reasoning and inference New York: Cambridge University Press 2000
4. Causal inference in the health sciences: a conceptual introduction. Pearl J, Health services and outcomes research methodology 2002 2 189 220
5. Causality and evidence discovery in epidemiology. Joffe M, Explanation, Prediction, and Confirmation. New Trends and Old Ones Reconsidered Springer, Dieks D, Wenceslao JG, Hartmann S, Uebel T, Weber M, 2011
6. The gap between evidence discovery and actual causal relationships. Joffe M, Preventive Medicine 2011 53 246 49 10.1016/j.ypmed.2011.07.011 21839767
7. Causal diagrams for epidemiologic research. Greenland S, Pearl J, Robins JM, Epidemiol 1999 10 37 48 10.1097/00001648-199901000-00008 (Pubitemid 29014427)
8. Data, design, and background knowledge in etiologic inference. Robins JM, Epidemiology 2001 11 313 20 (Pubitemid 32336395)
9. Chain graph models and their causal interpretations. Lauritzen SL, Richardson TS, J R Statist Soc B 2002 64 321 61 10.1111/1467-9868.00340
10. Estimating causal effects. Maldonado G, Greenland S, Int J Epidemiol 2002 31 422 29 10.1093/ije/31.2.422 11980807 (Pubitemid 34537803)
11. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA, Am J Epidemiol 2002 155 176 84 10.1093/aje/155.2.176 11790682 (Pubitemid 34072741)
12. A structural approach to selection bias. Hernán MA, Hernández-Díaz S, Robins JM, Epidemiol 2004 15 615 25 10.1097/01.ede.0000135174.63482.43 (Pubitemid 40416916)
13. Potential confounding by exposure history and prior outcomes-an example from perinatal epidemiology. Howards PP, Schisterman EF, Heagerty PJ, Epidemiology 2007 18 544 51 10.1097/EDE.0b013e31812001e6 17879426 (Pubitemid 47263026)
14. Causal diagrams. Glymour MM, Greenland S, Modern epidemiology Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins, Rothman KJ, Greenland S, Lash TL, 2008
15. Causal directed acyclic graphs and the direction of unmeasured confounding bias. VanderWeele TJ, Hernán MA, Robins JM, Epidemiol 2008 19 720 28 10.1097/EDE.0b013e3181810e29
16. Bringing causal models into the mainstream. Hogan JW, Epidemiology 2009 20 431 32 10.1097/EDE.0b013e3181a0997a 19363354
17. Beware of the DAG. Dawid AP, JMLR: workshop and conference proceedings 2009 6 59 86 http://jmlr.csail.mit.edu/proceedings/papers/v6/dawid10a/dawid10a. pdf [accessed 1 February 2012]
18. Spirtes P, Glymour C, Scheines R, Causation, prediction and search New York: Springer-Verlag 2
19. The method of path coefficients. Wright S, Annals of Mathematical Statistics 1934 5 161 215 10.1214/aoms/1177732676
20. A guide to econometrics. Kennedy P, Oxford: Blackwell Publishers Ltd 4 1998
21. Anderson RM, May RM, Infectious diseases of humans: dynamics and control Oxford: Oxford University Press 1992
22. Sterman JD, Business dynamics Boston: Irwin McGraw-Hill 2000
23. von Bertallanfy L, General system theory New York: George Braziller 1968
24. Ross R, The prevention of malaria New York: E.P. Dutton & company 1910
25. Stochastic Models for Epidemics with Special Reference to AIDS. Isham V, Ann Appl Probab 1993 3 1 27 10.1214/aoap/1177005505
26. Balancing sexual partnerships in an age and activity stratified model of HIV transmission in heterosexual populations. Garnett GP, Anderson RM, IMA J Math Appl Med Biol 1994 11 161 92 10.1093/imammb/11.3.161 7822888 (Pubitemid 2162041)
27. Modelling patterns of clearance of HPV-16 infection and vaccination efficacy. Baussano I, Garnett G, Segnan N, Ronco G, Vineis P, Vaccine 2011 29 1270 77 10.1016/j.vaccine.2010.11.082 21145375
28. An application of hidden Markov models to the French variant Creutzfeldt-Jakob disease epidemic. Chadeau-Hyam M, Guihenneuc-Jouyaux C, Cousens SN, et al. J Roy Stat Soc C (App Stat) 2010 59 839 53 10.1111/j.1467-9876.2010.00714.x
29. Integrating biomarkers into molecular epidemiological studies. Vineis P, Chadeau-Hyam M, Current Opinion in Oncology 2011 23 100 05 10.1097/CCO. 0b013e3283412de0 21076303
30. Complementing the genome with an 'exposome': The outstanding challenge of environmental exposure measurement in molecular epidemiology. Wild CP, Cancer Epidemiol Biomarkers Prev 2005 14 1847 50 10.1158/1055-9965.EPI-05-0456 16103423 (Pubitemid 41161186)
31. Environment and disease risks. Rappaport SM, Smith MT, Science 2010 330 460 41 10.1126/science.1192603 20966241
32. A framework for integrated environmental health impact assessment of systemic risks. Briggs DJ, Environ Health 2008 7 61 10.1186/1476-069X-7-61 19038020
33. Rose G, The strategy of preventive medicine Oxford: Oxford University Press 1992
34. Four types of effect modification: a classification based on directed acyclic graphs. VanderWeele TJ, Robins JM, Epidemiol 2007 18 561 68 10.1097/EDE.0b013e318127181b (Pubitemid 47263028)
35. Can DAGs clarify effect modification? Weinberg CR, Epidemiol 2007 18 569 72 10.1097/EDE.0b013e318126c11d (Pubitemid 47263029)
36. On the distinction between interaction and effect modification. VanderWeele TJ, Epidemiol 2009 20 863 71 10.1097/EDE.0b013e3181ba333c
37. The long reach of childhood health and circumstance: evidence from the Whitehall II Study. Case A, Paxson C, Economic Journal 2011 121 183 F204 10.1111/j.1468-0297.2011.02447.x
38. MacMahon B, Pugh TF, Epidemiology principles and methods Boston: Little Brown and Co 1970
39. Epidemiology and the web of causation: has anyone seen the spider? Krieger N, Soc Sci Med 1994 39 887 903 10.1016/0277-9536(94)90202-X 7992123 (Pubitemid 24264502)
40. Complex causal process diagrams for analyzing the health impacts of policy interventions. Joffe M, Mindell J, Am J Public Health 2006 96 473 79 10.2105/AJPH.2005.063693 16449586 (Pubitemid 43320693)
41. The need for strategic health assessment. Joffe M, Eur J Public Health 2008 18 439 40 10.1093/eurpub/ckn088 18809595
42. The role of strategic health impact assessment in sustainable development and green economics. Joffe M, International Journal of Green Economics 2010 4 1 16
43. Education level is a strong predictor of the risk of type 2 diabetes. The EPIC-InterAct Study. Sacerdote C, Ricceri F, Rolandsson O, Baldi I, Chirlaque MD, Feskens E, et al. Int J Epidemiol, in revision
44. Use of causal diagrams in systems epidemiology: elucidating the inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa. Rehfuess EA, Best N, Briggs DJ, Joffe M, Submitted to Emerging Themes in Epidmiology
45. Improving health through policies that promote active travel: a review of evidence to support integrated health impact assessment. de Nazelle A, Nieuwenhuijsen MJ, Antá JM, Brauer M, Briggs DJ, Braun-Fahrlander C, et al. Environment International doi: 10.1016/j.envint.2011.02.003
46. Social variation in biological fertility. (manuscript in preparation). Best N, Joffe M, Key J, Keiding N, Jensen TK
47. Modeling markers of disease progression by a hidden Markov process: application to characterizing CD4 cell decline. Guihenneuc-Jouyaux C, Richardson S, Longini IM Jr, Biometrics 2000 56 733 41 10.1111/j.0006-341X.2000.00733.x 10985209
48. Modelling complexity in health and social sciences: Bayesian graphical models as a tool for combining multiple sources of information. Best N, Jackson C, Richardson S, Proceedings of the 3 rd ASC International Conference on Survey Research Methods, Banks R, Cornelius R, Evans S, Manners T, 2005
49. Assessment and validation of exposure to disinfection by-products during pregnancy, in an epidemiological study examining associated risk of adverse fetal growth outcomes. Smith R, PhD thesis Imperial College London, Department of Epidemiology and Biostatistics 2011
50. An introduction to instrumental variables for epidemiologists. Greenland S, Int J Epidemiol 2000 29 722 29 10.1093/ije/29.4.722 10922351 (Pubitemid 30612241)
51. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? Davey Smith G, Ebrahim S, BMJ 2005 330 1076 79 10.1136/bmj.330.7499.1076 15879400 (Pubitemid 40646565)
52. Alcohol intake and blood pressure: a systematic review implementing a Mendelian randomization approach. Chen L, Davey Smith G, Harbord RM, Lewis SJ, PLoS Medicine 2008 5 52:0461 71 http://www.plosmedicine.org/article/info:doi/10. 1371/journal.pmed.0050052 [accessed 1 February 2012] (Pubitemid 351471695)
53. C-reactive protein and the risk of cancer: a mendelian randomization study. Allin KH, Nordestgaard BG, Zacho J, Tybjaerg-Hansen A, Bojesen SE, J Natl Cancer Inst 2010 102 202 06 10.1093/jnci/djp459 20056955
54. Does elevated C-reactive protein increase atrial fibrillation risk? A Mendelian randomization of 47,000 individuals from the general population. Marott SC, Nordestgaard BG, Zacho J, Friberg J, Jensen GB, Tybjaerg-Hansen A, Benn M, J Am Coll Cardiol 2010 56 789 95 10.1016/j.jacc.2010.02.066 20797493
55. Children and their parents' labor supply: Evidence from exogenous variation in family size. Angrist J, Evans W, American Economic Review 1998 88 450 77 (Pubitemid 128453610)
56. Effect of air-pollution control on death rates in Dublin, Ireland: an intervention study. Clancy L, Goodman P, Sinclair H, Dockery DW, Lancet 2002 360 1210 14 10.1016/S0140-6736(02)11281-5 12401247 (Pubitemid 35232720)
57. Wilkinson R, Pickett K, The Spirit Level: why equality is better for everyone London: Penguin 2010
58. The environment and disease: association or causation? Hill AB, Proc Royal Soc Med 1965 58 295 300
59. A framework for the evidence base to support Health Impact Assessment. Joffe M, Mindell J, J Epidemiol Community Health 2002 56 132 38 10.1136/jech.56.2.132 11812813 (Pubitemid 34098466)
60. Adams J, Risk London: UCL Press 1995
61. Causal thinking and complex system approaches in epidemiology. Galea S, Riddle M, Kaplan GA, Int J Epidemiol 2010 39 97 106 10.1093/ije/dyp296 19820105
62. Vensim. Ventana Systems, Inc. http://www.vensim.com/ [accessed 1 February 2012]
63. Counterintuitive behaviour of social systems. Forrester JW, Collected papers of Jay W Forrester: collectio Cambridge, MA: Wright-Allen Press 1975 1970 211 44
64. The power of the bond between cause and effect. Lane DC, System Dynamics Review 2007 23 95 118 10.1002/sdr.370 (Pubitemid 350148312)
65. Health, livelihoods, and nutrition in low-income rural systems. Joffe M, Food Nut Bull 2007 28 (suppl.) 227 36
66. Tuning in to the signals: noncoding sequence conservation in vertebrate genomes. Elgar G, Vavouri T, Trends Genet 2008 24 344 52 10.1016/j.tig.2008.04. 005 18514361
67. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EI, Wojtovich AP, Elliott SJ, Schaus SE, Collins JJ, Nature Biotechnology 2005 23 377 83 10.1038/nbt1075 15765094 (Pubitemid 41094431)