Negative Control Outcomes and the Analysis of Standardized Mortality Ratios

Epidemiology

Volumn 26, Issue 5, 2015, Pages 727-732

  Richardson, David B ^a   Keil, Alexander P ^a   Tchetgen Tchetgen, Eric ^b   Cooper, Glinda ^c  

^a The University of North Carolina at Chapel Hill   (United States)

^b HARVARD UNIVERSITY   (United States)

^c U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADJUSTED STANDARDIZED MORTALITY RATIO; ADULT; ARTICLE; BLADDER CANCER; CANCER MORTALITY; CONTROLLED STUDY; HUMAN; LABORATORY PERSONNEL; MAJOR CLINICAL STUDY; MALE; NEGATIVE CONTROL OUTCOME; OCCUPATIONAL CANCER; OUTCOME VARIABLE; PRIORITY JOURNAL; SALARY; STANDARDIZED MORTALITY RATIO; AGED; BLADDER TUMOR; EPIDEMIOLOGY; EVALUATION STUDY; FOLLOW UP; MIDDLE AGED; MORTALITY; OCCUPATIONAL DISEASE; STATISTICAL BIAS; STATISTICAL MODEL; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; BIAS (EPIDEMIOLOGY); EPIDEMIOLOGIC RESEARCH DESIGN; FOLLOW-UP STUDIES; HUMANS; MALE; MIDDLE AGED; MODELS, STATISTICAL; OCCUPATIONAL DISEASES; URINARY BLADDER NEOPLASMS;

EID: 84940023208     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000353     Document Type: Article

References (25)

1. International Agency for Research on Cancer. Preamble: IARC Monographs on the Evaluation of Carcinogenic Risks to Humans (Amended January 2006). Lyon, France: IARC; 2006.
2. Bucher JR. National Toxicology Program (NTP) final process for preparation of the report on carcinogens (RoC). Federal Register. 2012;77:1707.
3. Geiger HJ, Rush D, Michaels D. Dead Reckoning: A Critical Review of the Department of Energy's Epidemiologic Research. Washington, DC: Physicians for Social Responsibility; 1992.
4. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21:383-388.
5. Checkoway H, Pearce N, Kriebel D. Research Methods in Occupational Epidemiology. Monographs in Epidemiology and Biostatistics. 2nd ed. Oxford, England: Oxford University Press; 2004.
6. Breslow NE, Day NE. Statistical Methods in Cancer Research: The Design and Analysis of Cohort Studies. Vol II. Lyon, France: International Agency for Research on Cancer; 1987.
7. McNamee R. Regression modelling and other methods to control confounding. Occup Environ Med. 2005;62:500-506.
8. Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol. 2002;31:422-429.
9. Tchetgen Tchetgen E. The control outcome calibration approach for causal inference with unobserved confounding. Am J Epidemiol. 2014;179:633-640.
10. Richardson DB, Laurier D, Schubauer-Berigan MK, Tchetgen Tchetgen E, Cole SR. Assessment and indirect adjustment for confounding by smoking in cohort studies using relative hazards models. Am J Epidemiol. 2014;180:933-940.
11. Robinson CF, Schnorr TM, Cassinelli RT 2nd, et al. Tenth revision U.S. mortality rates for use with the NIOSH life table analysis system. J Occup Environ Med. 2006;48:662-667.
12. Schubauer-Berigan MK, Hein MJ, Raudabaugh WM, et al. Update of the NIOSH life table analysis system: a person-years analysis program for the windows computing environment. Am J Ind Med. 2011;54:915-924.
13. Steenland K, Beaumont J, Spaeth S, et al. New developments in the life table analysis system of the national institute for occupational safety and health. J Occup Med. 1990;32:1091-1098.
14. Vrijheid M, Cardis E, Blettner M, et al. The 15-country collaborative study of cancer risk among radiation workers in the nuclear industry: design, epidemiological methods and descriptive results. Radiat Res. 2007;167:361-379.
15. Park RM, Maizlish NA , Punnett L, Moure-Eraso R, Silverstein MA. A comparison of PMRs and SMRs as estimators of occupational mortality. Epidemiology. 1991;2:49-59.
16. Richardson DB, Cole SR, Chu H. Random effects regression models for trends in standardised mortality ratios. Occup Environ Med. 2013;70:133-139.
17. Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168:656-664.
18. Tchetgen Tchetgen EJ, Sofer T, Richardson D. Negative outcome control for unobserved confounding under a Cox Proportional Hazards Model. July 2015. Harvard University Biostatistics Working Paper Series. Working Paper 192. Available at: http://biostats.bepress.com/harvardbiostat/paper192.
19. Clapp RW. Mortality among US employees of a large computer manufacturing company: 1969-2001. Environ Health. 2006;5:30.
20. Stewart W, Hunting K. Mortality odds ratio, proportionate mortality ratio, and healthy worker effect. Am J Ind Med. 1988;14:345-353.
21. Wang JD, Miettinen OS. The mortality odds ratio (MOR) in occupational mortality studies-selection of reference occupation(s) and reference cause(s) of death. Ann Acad Med Singapore. 1984;13(2 Suppl):312-316.
22. Miettinen OS, Wang JD. An alternative to the proportionate mortality ratio. Am J Epidemiol. 1981;114:144-148.
23. Buckley JP, Keil AP, McGrath LJ, Edwards JK. Evolving methods for inference in the presence of healthy worker survivor bias. Epidemiology. 2015;26:204-212.
24. Richardson DB. Occupational exposures and lung cancer: adjustment for unmeasured confounding by smoking. Epidemiology. 2010;21:181-186.
25. Steenland K, Beaumont J, Halperin W. Methods of control for smoking in occupational cohort mortality studies. Scand J Work Environ Health. 1984;10:143-149.