Bayesian bias adjustments of the lung cancer SMR in a cohort of German carbon black production workers

Journal of Occupational Medicine and Toxicology

Volumn 5, Issue 1, 2010, Pages

  Morfeld, Peter ^a,b   McCunney, Robert J ^c  

^a UNIVERSITY OF COLOGNE   (Germany)

^b Institute for Occupational Epidemiology and Risk Assessment   (Germany)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARBON; SILICON DIOXIDE;

ARTICLE; BAYES THEOREM; CANCER MORTALITY; CASE CONTROL STUDY; COHORT ANALYSIS; CONTROLLED STUDY; DUST EXPOSURE; HUMAN; INDUSTRIAL WORKER; LUNG CANCER; MAJOR CLINICAL STUDY; MALE; MONTE CARLO METHOD; OCCUPATIONAL HAZARD; SMOKING; STANDARDIZED MORTALITY RATIO;

EID: 77955379563     PISSN: None     EISSN: 17456673     Source Type: Journal    
DOI: 10.1186/1745-6673-5-23     Document Type: Article

References (66)

1. Printing processes and printing inks, carbon black and some nitro compounds. International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic risks to humans Lyon: IARC 1996 65 1 578
2. Comparison of CTL reactivity in the spleen and draining lymph nodes after immunization with peptides pulsed on dendritic cells or mixed with Freund's incomplete adjuvant. M-J Wang MH Nissen S Buus C Röpke MH Classon, Immunology Letters 2003 90 13 18 10.1016/S0165-2478(03)00158-5 14611902
3. Carbon black. RJ McCunney HJ Muranko PA Valberg, Patty's Toxicology New York: John Wiley & Sons, Bingham E, Cohrssen B, Powell CH, Fifth 2001 8 1081 1101
4. Carcinogenic hazards from inhaled carbon black, titanium dioxide, and talc not containing asbestos or asbestiform fibers: recent evaluations by an IARC Monographs Working Group. RA Baan, Inhal Toxicol 2007 19 Suppl 1 213 228 10.1080/08958370701497903 17886070
5. Lung cancer mortality and carbon black exposure: uncertainties of SMR analyses in a cohort study at a German carbon black production plant. P Morfeld SF Büchte RJ McCunney C Piekarski, J Occup Environ Med 2006 48 1253 1264 10.1097/01.jom.0000215344.77132.99 17159642 (Pubitemid 44970343)
6. Cancer mortality in German carbon black workers 1976-1998. J Wellmann SK Weiland G Neiteler G Klein K Straif, Occup Environ Med 2006 63 513 521 10.1136/oem.2006.026526 16497850
7. Lung cancer mortality and carbon black exposure: cox regression analysis of a cohort from a German carbon black production plant. P Morfeld SF Büchte J Wellmann RJ McCunney C Piekarski, J Occup Environ Med 2006 48 1230 1241 10.1097/01.jom.0000215282.23531.b9 17159640 (Pubitemid 44967437)
8. Lung cancer mortality and carbon black exposure: a nested case-control study at a German carbon black production plant. SF Büchte P Morfeld J Wellmann U Bolm-Audorff RJ McCunney C Piekarski, J Occup Environ Med 2006 48 1242 1252 10.1097/01.jom.0000215710.17519.cd 17159641 (Pubitemid 44967438)
9. KJ Rothman S Greenland TL Lash, Modern epidemiology Philadelphia: Lippincott Williams & Wilkins 3 2008
10. Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment. S Greenland, Risk Anal 2001 21 579 583 10.1111/0272-4332.214136 11726013
11. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods. S Greenland, Int J Epidemiol 2009 38 1662 1673 10.1093/ije/dyp278 19744933
12. Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. K Steenland S Greenland, Am J Epidemiol 2004 160 384 392 10.1093/aje/kwh211 15286024
13. Bayesian modeling of lung cancer risk and bitumen fume exposure adjusted for unmeasured confounding by smoking. F de Vocht H Kromhout G Ferro P Boffetta I Burstyn, Occup Environ Med 2008 22
14. PM Lee, Bayesian statistics: an introduction London: Arnold 2 2003
15. Bayesian perspectives for epidemiological research: I. Foundations and basic methods. S Greenland, Int J Epidemiol 2006 35 765 775 10.1093/ije/dyi312 16446352
16. Bayesian perspectives for epidemiological research. II. Regression analysis. S Greenland, Int J Epidemiol 2007 36 195 202 10.1093/ije/dyl289 17329317
17. The philosophy of statistics. DV Lindley, The Statistician 2000 49 Part 3 293 337
18. Probability logic and probabilistic induction. S Greenland, Epidemiol 1998 9 322 332 10.1097/00001648-199805000-00018
19. NE Breslow NE Day, Statistical methods in cancer research. Volume II-The design and analysis of cohort studies Lyon: International Agency for Research on Cancer 1987
20. Adjusting a relative-risk estimate for study imperfections. G Maldonado, J Epidemiol Community Health 2008 62 655 663 10.1136/jech.2007.063909 18559450
21. Smoking and lung cancer: recent evidence and a discussion of some questions. J Cornfield W Haenszel EC Hammond AM Lilienfeld MB Shimkin EL Wynder, J Natl Cancer Inst 1959 22 173 203 13621204
22. Smoking and lung cancer: recent evidence and a discussion of some questions. 1959. J Cornfield W Haenszel EC Hammond AM Lilienfeld MB Shimkin EL Wynder, Int J Epidemiol 2009 38 1175 1191 10.1093/ije/dyp289 19773415
23. Spurious effects from an extraneous variable. ID Bross, J Chronic Dis 1966 19 637 647 10.1016/0021-9681(66)90062-2 5966011
24. Case-control studies: assessing the effect of a confounding factor. T Yanagawa, Biometrika 1984 71 191 194 10.1093/biomet/71.1.191
25. Indirect methods of assessing the effects of tobacco use in occupational studies. O Axelson K Steenland, Am J Ind Med 1988 13 105 118 10.1002/ajim.4700130107 3344750
26. KJ Rothman S Greenland, Modern epidemiology Philadelphia: Lippincott - Raven 2 1998
27. Das Rauchverhalten in Deutschland. B Junge M Nagel, Gesundheitswesen 1999 61 Sonderheft 2 121 125
28. Bundes-Gesundheitssurvey: Response, Zusammensetzung der Teilnehmer und Non-Responder-Analyse. W Thefeld H Stolzenberg B-M Bellach, Gesundheitswesen 1999 61 Sonderheft 2 57 S61 10726397
29. CAREX, 2005 http://www.ttl.fi/en/chemical-safety/carex/pages/default.aspx
30. Occupational exposure to carcinogens in the European Union. T Kauppinen J Toikkanen D Pedersen R Young W Ahrens P Boffetta J Hansen H Kromhout J Maqueda Blasco D Mirabelli,, et al. Occup Environ Med 2000 57 10 18 10.1136/oem.57.1.10 10711264
31. WR Gilks S Richardson DJ Spiegelhalter (Eds), Markov chain Monte Carlo in practice Coca Raton: Chapman & HALL/CRC 1996
32. Equations of state calculations by fast computing machines. N Metropolis AW Rosenbluth MN Rosenbluth AH Teller E Teller, J Chem Phys 1953 21 1087 1092 10.1063/1.1699114
33. Monte Carlo sampling methods using Markow chains and their applications. WK Hastings, Biometrika 1970 57 97 109 10.1093/biomet/57.1.97
34. K Newman, Bayesian inference (Lecture Notes) University of St. Andrews 2005 http://creem2.st-andrews.ac.uk/default.aspx
35. SAS/STAT(R) 9.2, User's Guide, Second Edition: Introduction to Bayesian Analysis Procedures: Assessing Markov Chain Convergence 2009 http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/ statug-introbayes-sect008.htm
36. R: A Language and environment for statistical computing. R Development Core Team, Vienna, Austria Foundation for statistical computing 2008 http://www.R-project.org
37. Smoking vs other risk factors as the cause of smoking-attributable deaths: confounding in the courtroom. MJ Thun LF Apicella SJ Henley, JAMA 2000 284 706 712 10.1001/jama.284.6.706 10927778
38. A comparison of PMRs and SMRs as estimators of occupational mortality. RM Park NA Maizlish L Punnett R Moure-Eraso MA Silverstein, Epidemiol 1991 2 49 59 10.1097/00001648-199101000-00008
39. Methodological issues regarding confounding and exposure misclassification in epidemiological studies of occupational exposures. A Blair P Stewart JH Lubin F Forastiere, Am J Ind Med 2007 50 199 207 10.1002/ajim.20281 17096363
40. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Z Fewell G Davey Smith JA Sterne, Am J Epidemiol 2007 166 646 655 10.1093/aje/kwm165 17615092
41. Pooled exposure-response analyses and risk assessment for lung cancer in 10 cohorts of silica-exposed workers: an IARC multicentre study. K Steenland A Mannetje P Boffetta L Stayner M Attfield J Chen M Dosemeci N DeKlerk E Hnizdo R Koskela H Checkoway, Cancer Causes Control 2001 12 773 784 Erratum in:Cancer Causes & Control 2002;2013:2777 10.1023/A:1012214102061 11714104
42. National job-exposure matrix in analyses of census-based estimates of occupational cancer risk. E Pukkala J Guo P Kyyronen M-L Lindbohm M Sallmen T Kauppinen, Scand J Work Environ Health 2005 31 97 107 15864903
43. Is exposure to silica associated with lung cancer in the absence of silicosis? A meta-analytical approach to an important public health question. TC Erren CB Glende P Morfeld C Piekarski, Int Arch Occup Environ Health 2008 8
44. IARC, Tobacco smoke and involuntary smoking Lyon: International Agency for Research on Cancer 2004
45. Epidemiological evidence on the carcinogenicity of silica: factors in scientific judgement. CA Soutar A Robertson BG Miller A Searl J Bignon, Ann Occup Hyg 2000 44 3 14 10.1016/S0003-4878(99)00047-2 10689755
46. Problems due to small samples and sparse data in conditional logistic regression analysis. S Greenland JA Schwartzbaum WD Finkle, Am J Epidemiol 2000 151 531 539 10707923
47. Bayesian sensitivity analysis for unmeasured confounding in observational studies. LC McCandless P Gustafson A Levy, Stat Med 2007 26 2331 2347 10.1002/sim.2711 16998821
48. Years of life lost due to exposure: causal concepts and empirical shortcomings. P Morfeld, Epidemiol Perspect Innov 2004 http://www.epi- perspectives.com/content/1/1/5 15601477
49. G-estimation of causal effects: isolated systolic hypertension and cardiovascular death in the Framingham Heart Study. JCM Witteman RB D'Agostino T Stijnen WB Kannel JC Cobb MA de Ridder A Hofman JM Robins, Am J Epidemiol 1998 148 390 401 9717884
50. Causal inference from complex longitudinal data. JM Robins, Latent variable modeling with applications to causality New York: Springer-Verlag, Berkane M, 1997 69 117
51. Adjusting for differential rates of prophylayis therapy for PCP in high- versus low-dose AZT treatment arms in an AIDS randomized trial. JM Robins S Greenland, J Am Statist Ass 1994 89 737 749 10.2307/2290899
52. th International Symposium Epidemiology. P Morfeld K Lampert M Emmerich C Stegmaier C Piekarski, Med Lav 2002 93 373 12371410
53. Putting background information about relative risks into conjugate prior distributions. S Greenland, Biometrics 2001 57 663 670 10.1111/j.0006-341X.2001. 00663.x 11550913
54. Bayesian model averaging in time-series studies of air pollution and mortality. DC Thomas M Jerrett N Kuenzli TA Louis F Dominici S Zeger J Schwarz RT Burnett D Krewski D Bates, J Toxicol Environ Health A 2007 70 311 315 10.1080/15287390600884941 17365593
55. Integrating studies on carcinogenic risk of carbon black: epidemiology, animal exposures, and mechanism of action. PA Valberg CM Long SN Sax, J Occup Environ Med 2006 48 1291 1307 10.1097/01.jom.0000215342.52699.2a 17159645
56. A comparative dose-related response of several key pro- and antiinflammatory mediators in the lungs of rats, mice, and hamsters after subchronic inhalation of carbon black. JM Carter N Corson KE Driscoll A Elder JN Finkelstein JN Harkema R Gelein P Wade-Mercer K Nguyen G Oberdorster, J Occup Environ Med 2006 48 1265 1278 10.1097/01.jom.0000230489.06025.14 17159643
57. The relevance of the rat lung response to particle overload for human risk assessment: A workshop consensus report - Risk Science Institute Workshop. ILSI, Inhal Toxicol 2000 12 1 17
58. Heuristic thinking and inference from observational epidemiology. TL Lash, Epidemiol 2007 18 67 72 10.1097/01.ede.0000249522.75868.16 (Pubitemid 46005911)
59. Risk of lung cancer following exposure to carbon black, titanium dioxide and talc: results from two case-control studies in Montreal. AV Ramanakumar M- Parent B Latreille J Siemiatycki, Int J Cancer 2008 122 183 189 10.1002/ijc.23021 17722096
60. The impact of prior distributions of uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia. S Greenland, J Am Stat Assoc 2003 98 47 54 10.1198/01621450338861905 (Pubitemid 36446640)
61. Why most discovered true associations are inflated. JPA Ioannidis, Epidemiol 2008 19 640 648 10.1097/EDE.0b013e31818131e7
62. Transposed conditionals, shrinkage, and direct and indirect unbiasedness. S Senn, Epidemiol 2008 19 652 654 discussion 657-658 10.1097/EDE. 0b013e318181b3e3
63. False-positive results in cancer epidemiology: a plea for epistemological modesty. P Boffetta JK McLaughlin C La Vecchia RE Tarone L Lipworth WJ Blot, J Natl Cancer Inst 2008 100 988 995 10.1093/jnci/djn191 18612135
64. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. J Wakefield, Am J Hum Genet 2007 81 208 227 10.1086/519024 17668372
65. Intelligent smoothing using hierarchical Bayesian models. P Graham, Epidemiol 2008 19 493 495 10.1097/EDE.0b013e31816b7859 (Pubitemid 351543530)
66. Bayesian methods for correcting misclassification. An example from birth defects epidemiology. RF MacLehose AF Olshan AH Herring MA Honein GM Shaw PA Romitti, Epidemiol 2009 20 27 35 10.1097/EDE.0b013e31818ab3b0