Reducing bias through directed acyclic graphs
Ian Shrier, Robert W Platt, Ian Shrier, Robert W Platt
Abstract
Background: The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.
Discussion: The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view.
Summary: Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.
Figures
References
- Rothman KJ, Greenland S. Causation and causal inference. In: Rothman KJ, Greenland S, editor. Modern Epidemiology. Vol. 2. Philadelphia: Lippencott-Raven Publishers; 1998. pp. 7–28.
- Hernan MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health. 2004;58:265–271. doi: 10.1136/jech.2002.006361.
- Greenland S, Morgenstern H. Confounding in health research. Annu Rev Public Health. 2001;22:189–212. doi: 10.1146/annurev.publhealth.22.1.189.
- Rothman KJ, Greenland S. Precision and validity in epidemiologic studies. In: Rothman KJ, Greenland S, editor. Modern Epidemiology. Vol. 2. Philadelphia: Lippencott-Raven Publishers; 1998. pp. 115–134.
- Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615–625. doi: 10.1097/01.ede.0000135174.63482.43.
- Glymour MM, Greenland S. Causal Diagrams. In: Rothman KJ, Greenland S, editor. Modern Epidemiology. Vol. 3. Philadelphia: Lippencott-Raven Publishers; 2008. pp. 183–209.
- Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14:300–306. doi: 10.1097/00001648-200305000-00009.
- Weinberg CR. Toward a clearer definition of confounding. Am J Epidemiol. 1993;137:1–8.
- Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48. doi: 10.1097/00001648-199901000-00008.
- Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000;11:561–570. doi: 10.1097/00001648-200009000-00012.
- Hernández-Díaz S, Schisterman EF, Hernán MA. The birth weight "paradox" uncovered? Am J Epidemiol. 2006;164:1115–1120. doi: 10.1093/aje/kwj275.
- Pearl J. Causality: models, reasoning and inference. Cambridge University of Cambridge; 2000. Simpson's paradox, confounding, and collapibility; pp. 173–200.
- Hernan MA, Hernandez-Diaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155:176–184. doi: 10.1093/aje/155.2.176.
- Pearl J. Causality: models, reasoning and inference. Cambridge University of Cambridge; 2000. The art and science of cause and effect; pp. 331–358.
- Pearl J. Causality: models, reasoning and inference. Cambridge University of Cambridge; 2000.
- Holland PW. Statistics and causal inference. J Amer Statist Assoc. 1986;81:945–960. doi: 10.2307/2289064.
- Spirtes P, Glymour C, Scheines R. Causation, prediction and search. Cambridge: MIT Press; 2000. Causation and prediction: axioms and explications; pp. 19–58.
- Greenland S, Rothman KJ. Introduction to stratified analysis. In: Rothman KJ, Greenland S, editor. Modern Epidemiology. Vol. 2. Philadelphia: Lippencott-Raven Publishers; 1998. pp. 253–279.
- Robins JM. The control of confounding by intermediate variables. Stats Med. 1989;8:679–701. doi: 10.1002/sim.4780080608.
- Pearl J. Causality: models, reasoning and inference. Cambridge University of Cambridge; 2000. Introduction to probabilities, graphs, and causal models; pp. 1–40.
- Spirtes P, Glymour C, Scheines R. Causation, prediction and search. Cambridge: MIT Press; 2000. Discovery algorithms for causally sufficient structures; pp. 73–122.
- Spirtes P, Glymour C, Scheines R. Causation, prediction and search. Cambridge: MIT Press; 2000. Discovery algorithms without causal sufficiency; pp. 123–155.
- Weinberg CR. Can DAGs clarify effect modification? Epidemiology. 2007;18:569–572. doi: 10.1097/EDE.0b013e318126c11d.
- VanderWeele TJ, Robins JM. Four types of effect modification: a classification based on directed acyclic graphs. Epidemiology. 2007;18:561–568. doi: 10.1097/EDE.0b013e318127181b.
- Vanderweele TJ, Robins JM. Directed Acyclic Graphs, Sufficient Causes, and the Properties of Conditioning on a Common Effect. Am J Epid. 2007;166:1096–1104. doi: 10.1093/aje/kwm179.
- Kaufman JS, Maclehose RF, Kaufman S. A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiol Perspect Innov. 2004;1
- Cole SR, Hernan MA. Fallibility in estimating direct effects. Int J Epidemiol. 2002;31:163–165. doi: 10.1093/ije/31.1.163.
- Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–560. doi: 10.1097/00001648-200009000-00011.
- Haight T, Tager I, Sternfeld B, Satariano W, Laan M van der. Effects of body composition and leisure-time physical activity on transitions in physical functioning in the elderly. Am J Epidemiol. 2005;162:607–617. doi: 10.1093/aje/kwi254.
- Witteman JC, D'Agostino RB, Stijnen T, Kannel WB, Cobb JC, de Ridder MA, Hofman A, Robins JM. G-estimation of causal effects: isolated systolic hypertension and cardiovascular death in the Framingham Heart Study. Am J Epidemiol. 1998;148:390–401.
Source: PubMed