Implications of m bias in epidemiologic studies: A simulation study

American Journal of Epidemiology

Volumn 176, Issue 10, 2012, Pages 938-948

  Wei, Liu ^a   Brookhart, M Alan ^a   Schneeweiss, Sebastian ^a   Mi, Xiaojuan ^a   Setoguchi, Soko ^a  

^a DUKE CLINICAL RESEARCH INSTITUTE   (United States)

Author keywords

bias (epidemiology); simulation

Indexed keywords

HYDROXYMETHYLGLUTARYL COENZYME A REDUCTASE INHIBITOR; SEROTONIN UPTAKE INHIBITOR;

DATA SET; EPIDEMIOLOGY; NUMERICAL MODEL; POLLUTION EXPOSURE; RISK FACTOR; THEORETICAL STUDY;

ARTICLE; CORONARY ARTERY DISEASE; DEPRESSION; DRUG USE; EPIDEMIOLOGY; HUMAN; HYPERCHOLESTEROLEMIA; LUNG CANCER; M BIAS; MONTE CARLO METHOD; SYSTEMATIC ERROR;

BIAS (EPIDEMIOLOGY); CAUSALITY; COHORT STUDIES; EPIDEMIOLOGIC METHODS; HUMANS; MODELS, THEORETICAL; RISK; RISK FACTORS;

EID: 84869057328     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kws165     Document Type: Article

References (38)

1. Glymour M, Greenland S. Causal diagrams. In: Rothman K, Greenland S, Lash T, eds. Modern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008:183-212.
2. Pearl J. Causality: Models, Reasoning and Inference. 2nd ed. New York, NY: Cambridge University Press; 2009.
3. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48.
4. Robins JM. Data, design, background knowledge in etiologic inference. Epidemiology. 2001;12(3):313-320.
5. Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82(4):669-88.
6. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14(3):300-306.
7. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615-625.
8. McCall NT, Parks P, Smith K, et al. The prevalence of major depression or dysthymia among aged Medicare fee-forservice beneficiaries. Int J Geriatr Psychiatry. 2002; 17(6):557-565.
9. Arday DR, Lapin P, Chin J, et al. Smoking patterns among seniors and the Medicare stop smoking program. J Am Geriatr Soc. 2002;50(10):1689-1697.
10. Ford ES, Mokdad AH, Giles WH, et al. Serum total cholesterol concentrations and awareness, treatment, and control of hypercholesterolemia among US adults: findings from the National Health and Nutrition Examination Survey, 1999 to 2000. Circulation. 2003;107(17):2185-2189.
11. Wulsin LR, Singal BM. Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review. Psychosom Med. 2003;65(2):201-210.
12. Dawber TR. The Framingham Study: The Epidemiology of Atherosclerotic Disease. Cambridge, MA: Harvard University Press; 1980.
13. National Cancer Institute. Table 15.1. Cancer of the lung and bronchus (invasive). In: SEER Cancer Statistics Review (1975-2006). Bethesda, MD: National Cancer Institute; 2007. (http://seer.cancer.gov/csr/1975-2006/results- merged/sect-15-lung-bronchus.pdf). (Accessed August 2, 2011).
14. Crystal S, Sambamoorthi U, Walkup JT, et al. Diagnosis and treatment of depression in the elderly Medicare population: predictors, disparities, and trends. J Am Geriatr Soc. 2003; 51(12):1718-1728.
15. Friedman G, Tekawa I, Sadler M, et al. Smoking and mortality: the Kaiser Permanente experience. In: Changes in Cigarette-related Disease Risks and Their Implications for Prevention and Control. (Smoking and tobacco monograph no. 8). Bethesda, MD: National Cancer Institute; 1997:477-497.
16. Wilson PW, DAgostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837-1847.
17. Wilson PW, Abbott RD, Castelli WP. High density lipoprotein cholesterol and mortality. The Framingham Heart Study. Arteriosclerosis. 1988;8(6):737-741.
18. Federman AD, Adams AS, Ross-Degnan D, et al. Supplemental insurance and use of effective cardiovascular drugs among elderly Medicare beneficiaries with coronary heart disease. JAMA. 2001;286(14):1732-1739.
19. Ma J, Sehgal NL, Ayanian JZ, et al. National trends in statin use by coronary heart disease risk category. PLoS Med. 2005;2(5):pe123. (doi:10.1371/journal.pmed.0020123).
20. Toh S, Rodríguez LA, Hernández-Díaz S. Use of antidepressants and risk of lung cancer. Cancer Causes Control. 2007;18(10):1055-1064.
21. Kuoppala J, Lamminpää A, Pukkala E. Statins and cancer: a systematic review and meta-analysis. Eur J Cancer. 2008; 44(15):2122-2132.
22. Burton A, Altman DG, Royston P, et al. The design of simulation studies in medical statistics. Stat Med. 2006; 25(24):4279-4292.
23. Cole SR, Platt RW, Schisterman EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010; 39(2):417-420.
24. Blais L, Desgagné A, LeLorier J. 3-Hydroxy-3-methylglutaryl coenzyme A reductase inhibitors and the risk of cancer: a nested case-control study. Arch Intern Med. 2000;160(15):2363-2368.
25. Graaf MR, Beiderbeck AB, Egberts AC, et al. The risk of cancer in users of statins. J Clin Oncol. 2004;22(12): 2388-2394.
26. Kaye JA, Jick H. Statin use and cancer risk in the General Practice Research Database. Br J Cancer. 2004;90(3): 635-637.
27. Coogan PF, Rosenberg L, Strom BL. Statin use and the risk of 10 cancers. Epidemiology. 2007;18(2):213-219.
28. Setoguchi S, Glynn RJ, Avorn J, et al. Statins and the risk of lung, breast, and colorectal cancer in the elderly. Circulation. 2007;115(1):27-33.
29. VanderWeele TJ, Robins JM. Directed acyclic graphs, sufficient causes, and the properties of conditioning on a common effect. Am J Epidemiol. 2007;166(9): 1096-1104.
30. Schneeweiss S, Rassen JA, Glynn RJ, et al. Highdimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512-522.
31. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127(8): 757-763.
32. Brookhart MA, Stürmer T, Glynn RJ, et al. Confounding control in healthcare database research: challenges and potential approaches. Med Care. 2010;48(6 suppl): S114-S120.
33. Brookhart MA, Schneeweiss S, Rothman KJ, et al. Variable selection for propensity score models. Am J Epidemiol. 2006;163(12):1149-1156.
34. Pearl J. On a class of bias-amplifying variables that endanger effect estimates. In: Grünwald P, Spirtes P, eds. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). Corvallis, OR: AUAI Press; 2010:417-424.
35. Bhattacharya J, Vogt W. Do Instrumental Variables Belong in Propensity Scores? (NBER technical working paper 343). Cambridge, MA: National Bureau of Economic Research; 2007.
36. Chodick G, Shalev V, Gerber Y, et al. Long-term persistence with statin treatment in a not-for-profit health maintenance organization: a population-based retrospective cohort study in Israel. Clin Ther. 2008;30(11): 2167-2179.
37. Pratt LA, Brody DJ, Gu Q. Antidepressant Use in Persons Aged 12 and Over: United States, 2005-2008. (NCHS data brief no. 76). Hyattsville, MD: National Center for Health Statistics; 2011.
38. Eaton CB, Lapane KL, Murphy JB, et al. Effect of statin (HMG-Co-A-reductase inhibitor) use on 1-year mortality and hospitalization rates in older patients with cardiovascular disease living in nursing homes. J Am Geriatr Soc. 2002; 50(8):1389-1395.