A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable

International Journal of Epidemiology

Volumn 46, Issue 3, 2017, Pages 1063-1072

  Corbin, Marine ^a,b   Haslett, Stephen ^a,c,d   Pearce, Neil ^a,e   Maule, Milena ^b   Greenland, Sander ^f  

^a MASSEY UNIVERSITY   (New Zealand)

^b UNIVERSITY OF TURIN   (Italy)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^d MASSEY UNIVERSITY   (New Zealand)

^e LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^f UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Direct imputation, fully Bayesian analysis; Lung cancer; Misclassification; Sensitivity specificity imputation; Smoking status

Indexed keywords

BAYESIAN ANALYSIS; CANCER; DATA SET; EPIDEMIOLOGY; MODEL VALIDATION; MONTE CARLO ANALYSIS; SENSITIVITY ANALYSIS; SMOKING;

ADULT; AGED; ALGORITHM; ARTICLE; BAYES THEOREM; CASE CONTROL STUDY; COMPARATIVE STUDY; CONTROLLED STUDY; DIRECT IMPUTATION; FEMALE; HUMAN; LUNG CANCER; MALE; MARKOV CHAIN; MEASUREMENT ERROR; MISCLASSIFICATION; PREDICTIVE VALUE; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SMOKING; CAUSALITY; LUNG TUMOR; MIDDLE AGED; MONTE CARLO METHOD; NEW ZEALAND; ODDS RATIO; REGISTER; STATISTICAL BIAS; UNCERTAINTY; YOUNG ADULT;

ADULT; AGED; BAYES THEOREM; BIAS; CASE-CONTROL STUDIES; CAUSALITY; FEMALE; HUMANS; LUNG NEOPLASMS; MALE; MARKOV CHAINS; MIDDLE AGED; MONTE CARLO METHOD; NEW ZEALAND; ODDS RATIO; REGISTRIES; SENSITIVITY AND SPECIFICITY; SMOKING; UNCERTAINTY; YOUNG ADULT;

EID: 85027732187     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyx027     Document Type: Article

References (29)

1. Rothman KJ, Greenland S, Lash TL. Validity in epidemiologic studies. In: Modern Epidemiology. 3rd edn.Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams&Wilkins, 2008.
2. Lash TL, FoxMP, Fink AK. Applying Quantitative Bias Analysis to Epidemiologic Data. New York, NY: Springer, 2009.
3. Greenland S. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods. Int J Epidemiol 2009;38:1662-73.
4. Gustafson P.Measurement Error andMisclassification in Statistics and Epidemiology. Boca Raton, FL: Chapman and Hall, 2003.
5. Cole SR, Chu H, Greenland S. Multiple-imputation for measurement-error correction. Int J Epidemiol 2006;35:1074-81.
6. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement Error in Nonlinear Models: A Modern Perspective 2nd edn. Boca Raton, FL: Chapman and Hall/CRC, 2006.
7. Little RJA, Rubin DB. In: Sons JW (ed). Statistical Analysis with Missing Data. New York, NY: 2002.
8. Allison PD. Missing Data. London: SAGE Publications, 2001.
9. Daniels MJ, Hogan JW. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Abingdon, UK: Taylor&Francis, 2008.
10. Tsiatis A. Semiparametric Theory and Missing Data. New York, NY: Springer, 2007.
11. Schafer JL. Analysis of Incomplete Multivariate Data. Abingdon, UK: Taylor&Francis, 1997.
12. Carpenter J, Kenward M. Missing data in randomised controlled trials-a practical guide. Birmingham, UK: National Institute for Health Research, 2007.
13. Greenland S, Lash TL. Bias analysis. In: Rothman KJ, Greenland S, Lash TL (eds). Modern Epidemiology. 3rd edn. Philadelphia, PA: Wolters KluwerHealth/LippincottWilliams&Wilkins, 2008.
14. Kleinbaum DG, Kupper LL, MorgensternH. Epidemiologic Research: Principles andQuantitativeMethods.Hoboken, NJ: Wiley, 1982.
15. MacLehose RF, Gustafson P. Is probabilistic bias analysis approximately Bayesian?. Epidemiology 2012;23:151-58.
16. Fox MP, Lash TL, Greenland S. A method to automate probabilistic sensitivity analyses of misclassified binary variables. Int J Epidemiol 2005;34:1370-76.
17. Greenland S. Multiple-bias modelling for analysis of observational data. J R Stat Soc 2005;168:267-91.
18. Chu H, Wang Z, Cole SR, Greenland S. Sensitivity analysis of misclassification: a graphical and a Bayesian approach. Ann Epidemiol 2006;16:834-41.
19. Corbin M, McLean D, Mannetje A et al. Lung cancer and occupation: A New Zealand cancer registry-based case-control study. Am J Ind Med 2011;54:89-101.
20. Patrick DL, Cheadle A, Thompson DC, Diehr P, Koepsell T, Kinne S. The validity of self-reported smoking: a review and meta-analysis. Am J Public Health 1994;84:1086-93.
21. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York, NY: Chapman&Hall, 1993.
22. Hinkley D. Jackknife methods. In:Encyclopedia of Statistical Sciences. Hoboken, NJ: John Wiley, 2004.
23. Blettner M, Wahrendorf J. What does an observed relative risk convey about possible misclassification?. Methods Inf Med 1984;23:37-40.
24. Greenland S. Bayesian perspectives for epidemiological research: I. Foundations and basic methods. Int J Epidemiol 2006;35: 765-75.
25. Greenland S. Bayesian perspectives for epidemiological research. II. Regression analysis. Int J Epidemiol 2007;36(1): 195-202.
26. Greenland S. Prior data for non-normal priors. Stat Med 2007;26(19):3578-90.
27. Pearce N, Corbin M. Why we should be Bayesians (and often already are without realising it). In: Venables K, editor. Current topics in occupational epidemiology. Oxford, UK: Oxford University press; 2013.
28. Fischer HJ, Greenland S, Kheifets L. Methods to explore uncertainty and bias introduced by job exposure matrices. Risk Anal. To appear;35.
29. Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol 2014;43(6):1969-85.