Use of causal diagrams to inform the design and interpretation of observational studies: An example from the study of heart and renal protection (SHARP)

Clinical Journal of the American Society of Nephrology

Volumn 12, Issue 3, 2017, Pages 546-552

  Staplin, Natalie ^a   Herrington, William G ^a   Judge, Parminder K ^a   Reith, Christina A ^a   Haynes, Richard ^a   Landray, Martin J ^a   Baigent, Colin ^a   Emberson, Jonathan ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Bias (Epidemiology); Causal diagrams; Chronic; Epidemiology and outcomes; Kidney; Kidney Failure; Motivation; Observational studies; Renal Insufficiency; Selection Bias; Smoking

Indexed keywords

AGE; ALCOHOL CONSUMPTION; ARTICLE; BLOOD PRESSURE; BODY MASS; DISEASE COURSE; EDUCATIONAL STATUS; END STAGE RENAL DISEASE; ETHNICITY; GENDER; HEART PROTECTION; HUMAN; HYPOTHESIS; INTERPRETATION BIAS; MICROALBUMINURIA; OBSERVATIONAL STUDY; RENAL PROTECTION; SELECTION BIAS; SMOKING; STATISTICAL MODEL; CHRONIC KIDNEY FAILURE; EPIDEMIOLOGY; METHODOLOGY; STATISTICAL ANALYSIS; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA;

BIAS; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; HUMANS; KIDNEY FAILURE, CHRONIC; OBSERVATIONAL STUDIES AS TOPIC; RESEARCH DESIGN; SMOKING;

EID: 85021751807     PISSN: 15559041     EISSN: 1555905X     Source Type: Journal    
DOI: 10.2215/CJN.02430316     Document Type: Article

References (25)

1. Cole SR, Platt RW, Schisterman EF, Chu H, Westreich D, Richardson D, Poole C: Illustrating bias due to conditioning on a collider. Int J Epidemiol 39: 417–420, 2010
2. Greenland S, Pearl J, Robins JM: Causal diagrams for epidemiologic research. Epidemiology 10: 37–48, 1999
3. Hernán MA, Hernández-Díaz S, Robins JM: A structural approach to selection bias. Epidemiology 15: 615–625, 2004
4. Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JA: Methods for dealing with time-dependent confounding. Stat Med 32: 1584–1618, 2013
5. Fewell Z, Hernán MA, Wolfe F, Tilling K, Choi H, Sterne JAC: Controlling for time-dependent confounding using marginal structural models. Stata J 4: 402–420, 2004
6. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA: Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Am J Epidemiol 155: 176–184, 2002
7. Williamson EJ, Aitken Z, Lawrie J, Dharmage SC, Burgess JA, Forbes AB: Introduction to causal diagrams for confounder selection. Respirology 19: 303–311, 2014
8. Shrier I, Platt RW: Reducing bias through directed acyclic graphs. BMC Med Res Methodol 8: 70–84, 2008
9. Textor J, Hardt J, Knüppel S: DAGitty: A graphical tool for analyzing causal diagrams. Epidemiology 22: 745, 2011
10. Breitling LP: dagR: A suite of R functions for directed acyclic graphs. Epidemiology 21: 586–587, 2010
11. Weng HY, Hsueh YH, Messam LLM, Hertz-Picciotto I: Methods of covariate selection: Directed acyclic graphs and the change-in-estimate procedure. Am J Epidemiol 169: 1182–1190, 2009
12. Evans D, Chaix B, Lobbedez T, Verger C, Flahault A: Combining directed acyclic graphs and the change-in-estimate procedure as a novel approach to adjustment-variable selection in epidemiology. BMC Med Res Methodol 12: 156–170, 2012
13. Baigent C, Landray MJ, Reith C, Emberson J, Wheeler DC, Tomson C, Wanner C, Krane V, Cass A, Craig J, Neal B, Jiang L, Hooi LS, Levin A, Agodoa L, Gaziano M, Kasiske B, Walker R, Massy ZA, Feldt-Rasmussen B, Krairittichai U, Ophascharoensuk V, Fellström B, Holdaas H, Tesar V, Wiecek A, Grobbee D, de Zeeuw D, Grönhagen-Riska C, Dasgupta T, Lewis D, Herrington W, Mafham M, Majoni W, Wallendszus K, Grimm R, Pedersen T, Tobert J, Armitage J, Baxter A, Bray C, Chen Y, Chen Z, Hill M, Knott C, Parish S, Simpson D, Sleight P, Young A, Collins R; SHARP Investigators: The effects of lowering LDL cholesterol with sim-vastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): A randomised placebo-controlled trial. Lancet 377: 2181–2192, 2011
14. Halimi JM, Giraudeau B, Vol S, Cacès E, Nivet H, Lebranchu Y, Tichet J: Effects of current smoking and smoking discontinuation on renal function and proteinuria in the general population. Kidney Int 58: 1285–1292, 2000
15. Pinto-Sietsma SJ, Mulder J, JanssenWM, Hillege HL, de ZeeuwD, de Jong PE: Smoking is related to albuminuria and abnormal renal function in nondiabetic persons. Ann Intern Med 133: 585–591, 2000
16. Hemmelgarn BR, Manns BJ, Lloyd A, James MT, Klarenbach S, Quinn RR, Wiebe N, Tonelli M; Alberta Kidney Disease Network: Relation between kidney function, proteinuria, and adverse outcomes. JAMA 303: 423–429, 2010
17. Gansevoort RT, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, Coresh J; Chronic Kidney Disease Prognosis Consortium: Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int 80: 93–104, 2011
18. Staplin N, Haynes R, Herrington WG, Reith C, Cass A, Fellström B, Jiang L, Kasiske BL, Krane V, Levin A, Walker R, Wanner C, Wheeler DC, Landray MJ, Baigent C, Emberson J; SHARP Collaborative Group: Smoking and adverse outcomes in patients with CKD: The Study of Heart and Renal Protection (SHARP) [published online ahead of print April 22, 2016]. Am J Kidney Dis doi:10.1053/j.ajkd.2016.02.052
19. Dahabreh IJ, Kent DM: Index event bias as an explanation for the paradoxes of recurrence risk research. JAMA 305: 822–823, 2011
20. Smits LJ, van Kuijk SM, Leffers P, Peeters LL, Prins MH, Sep SJ: Index event bias-a numerical example. J Clin Epidemiol 66: 192–196, 2013
21. Greenland S: Quantifying biases in causal models: Classical confounding vs collider-stratification bias. Epidemiology 14: 300–306, 2003
22. Arah OA, Chiba Y, Greenland S: Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders. Ann Epidemiol 18: 637–646, 2008
23. Clarke R, Shipley M, Lewington S, Youngman L, Collins R, Marmot M, Peto R: Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol 150: 341–353, 1999
24. Frost C, Thompson SG: Correcting for regression dilution bias: Comparison of methods for a single predictor variable. J R Stat Soc Ser A 163: 173–189, 2000
25. Shrier I: Estimating causal effect with randomized controlled trial. Epidemiology 24: 779–781, 2013