Probabilistic approaches to better quantifying the results of epidemiologic studies

International Journal of Environmental Research and Public Health

Volumn 7, Issue 4, 2010, Pages 1520-1539

  Gustafson, Paul ^a   McCandless, Lawrence C ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Confounding; Epidemiologic methods; Exposure misclassification; Selection bias; Sensitivity analysis

Indexed keywords

BAYES THEOREM; BIOSTATISTICS; EPIDEMIOLOGICAL DATA; MEASUREMENT ERROR; PROBABILITY; QUANTITATIVE ANALYSIS; RANDOM SAMPLE; REVIEW; STATISTICAL ANALYSIS; SYSTEMATIC ERROR; UNCERTAINTY; EPIDEMIOLOGY;

EPIDEMIOLOGIC STUDIES; PROBABILITY;

EID: 77954548554     PISSN: None     EISSN: 16604601     Source Type: Journal    
DOI: 10.3390/ijerph7041520     Document Type: Review

References (30)

1. Cornfield, J.; Haenszel, W.; Hammond, E.C.; Lilienfeld, A.M.; Shimkin, M.B.; Wynder, E.L. Smoking and lung cancer: recent evidence and a discussion of some questions. J. Nat. Cancer Inst. 1959, 22, 173-203.
2. Greenland, S. Sensitivity analysis, Monte-Carlo risk analysis, and Bayesian uncertainty assessment. Risk Anal. 2001, 21, 579-583.
3. Greenland, S. The impact of prior distributions for uncontrolled confounding and response bias: A case study of the relation of wire codes and magnetic fields to childhood leukemia. J. Amer. Statist. Assn. 2003, 98, 47-55.
4. Lash, T.L.; Fink, A.K. Semi-automated sensitivity analysis to assess systematic errors in observational data. Epidemiology 2003, 14, 451-458.
5. Steenland, K.; Greenland, S. Monte-Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. Amer. J. Epidemiol. 2004, 160, 384-392.
6. Greenland, S. Multiple-bias modelling for analysis of observational data. J. Royal Statist. Soc., A 2005, 168, 267-306.
7. Fox, M.P.; Lash, T.L.; Greenland, S. A method to automate probabilistic sensitivity analyses of misclassified binary variables. Int. J. Epidemiol. 2005, 34, 1370-1377.
8. Lash, T.L.; Fox, M.P.; Fink, A.K. Applying Quantitative Bias Analysis to Epidemiologic Data; Springer: Berlin, Germany, 2009.
9. Mezei, G.; Kheifets, L. Selection bias and its implications for case-control studies: a case study of magnetic field exposure and childhood leukaemia. Int. J. Epidemiol. 2006, 35, 397-406.
10. Hernan, M.A.; Hernández-D́iáz, S.; Robins, J.M. A structural approach to selection bias. Epidemiology 2004, 15, 615-625.
11. Fewell, Z.; Davey Smith, G.; Sterne, J.A.C. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Amer. J. Epidemiol. 2007, 166, 646-655.
12. Schneeweiss, S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol. Drug Saf. 2006, 15, 291-303.
13. Greenland, S. Bayesian perspectives for epidemiological research: III. Bias analysis via missing data methods. Int. J. Epidemiol. 2009, 38, 1662-1673.
14. MacMahon, B.; Yen, S.; Trichopoulos, D.; Warren, K.; Nardi, G. Coffee and cancer of the pancreas. N. Engl. J. Med. 1981, 304, 630-633.
15. Jewell, N.P. Statistics for Epidemiology. Chapman and Hall, CRC Press: Boca Raton, FL, USA 2003.
16. Wang, L.; Krieger, A.M. Causal conclusions are most sensitive to unobserved binary covariates. Statist. Med. 2006, 25, 2257-2271.
17. McCandless, L.C.; Gustafson, P.; Levy, A.R. Bayesian sensitivity analysis for unmeasured confounding in observational studies. Statist. Med. 2007, 26, 2331-2347.
18. McCandless, L.C.; Gustafson, P.; Levy, A.R. A sensitivity analysis using information about measured confounders yielded improved assessments of uncertainty from unmeasured confounding. J. Clin. Epidemiol. 2008, 61, 247-255.
19. Tavani, A.; La Vecchia, C. Coffee and cancer: A review of epidemiologic studies 1990-1999. Eur. J. Cancer Prev. 2000, 9, 241-256.
20. Rosenberg, L.; Slone, D.; Shapiro, S.; Kaufman, D.W.; Stolley, P.D.; Miettinen, O.S. Coffee drinking and myocardial infarction in young women. Amer. J. Epidemiol. 1980, 111, 675-681.
21. Geneletti, S.; Richardson, S.; Best, N. Adjusting for selection bias in retrospective case-control studies. Biostatistics 2009, 10, 17-31.
22. Flegal, K.M.; Keyl, P.M.; Nieto, F.J. Differential misclassification arising from nondifferential errors in exposure measurement. Amer. J. Epidemiol. 1991, 134, 1233-1246.
23. Gustafson, P.; Le, N.D. Comparing the effects of continuous and discrete covariate measurement error, with emphasis on the dichotomization of mismeasured predictors. Biometrics 2002, 58, 878-887.
24. Lunn, D.J.; Thomas, A.; Best, N.; Spiegelhalter, D. WinBUGS - A Bayesian modelling framework: concepts, structure, and extensibility. Statist. Comput. 2000, 10, 325-337.
25. Gustafson, P.; Le, N.D.; Saskin, R. Case-control analysis with partial knowledge of exposure misclassification probabilities. Biometrics 2001, 57, 598-609.
26. Greenland, S.; Gustafson, P. Accounting for independent nondifferential misclassification does not increase certainty than an observed association is in the correct direction. Amer. J. Epidemiol. 2006, 164, 63-68.
27. Gustafson, P.; Greenland, S. Curious phenomena in Bayesian adjustment for exposure misclassification. Statist. Med. 2006, 25, 87-103.
28. Rosenbaum, P.R. Heterogeneity and causality: unit heterogeneity and design sensitivity in observational studies. Amer. Statist. 2005, 59, 147-153.
29. Greenland, S. Prior data for non-normal priors. Statist. Med. 2007, 26, 3578-3590.
30. Orsini, N.; Bellocco, R.; Bottai, M.; Wolk, A.; Greenland, S. A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies. Stata J. 2008, 8, 29-48.