A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable.
Measurement error is an important source of bias in epidemiological studies. We illustrate three approaches to sensitivity analysis for the effect of measurement error: imputation of the 'true' exposure based on specifying the sensitivity and specificity of the measured exposure (SS); direct imputation (DI) using a regression model for the predictive values; and adjustment based on a fully Bayesian analysis.



We deliberately misclassify smoking status in data from a case-control study of lung cancer. We then implement the SS and DI methods using fixed-parameter (FBA) and probabilistic (PBA) bias analyses, and Bayesian analysis using the Markov-Chain Monte-Carlo program WinBUGS to show how well each recovers the original association.



The 'true' smoking-lung cancer odds ratio (OR), adjusted for sex in the original dataset, was OR = 8.18 [95% confidence limits (CL): 5.86, 11.43]; after misclassification, it decreased to OR = 3.08 (nominal 95% CL: 2.40, 3.96). The adjusted point estimates from all three approaches were always closer to the 'true' OR than the OR estimated from the unadjusted misclassified smoking data, and the adjusted interval estimates were always wider than the unadjusted interval estimate. When imputed misclassification parameters departed much from the actual misclassification, the 'true' OR was often omitted in the FBA intervals whereas it was always included in the PBA and Bayesian intervals.



These results illustrate how PBA and Bayesian analyses can be used to better account for uncertainty and bias due to measurement error.
Centre for Public Health Research, Massey University, Wellington, New Zealand.|Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy.
Marine
M
Corbin
Centre for Public Health Research, Massey University, Wellington, New Zealand.|Statistical Consulting Unit, Australian National University, Camberra, Australia.|Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand.
Stephen
S
Haslett
Centre for Public Health Research, Massey University, Wellington, New Zealand.|Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Neil
N
Pearce
Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy.
Milena
M
Maule
Department of Epidemiology and Department of Statistics, University of California, Los Angeles, CA, USA.
Sander
S
Greenland
D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D015982:Bias; D016022:Case-Control Studies; D015984:Causality; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009010:Monte Carlo Method; D009520:New Zealand / Q000453:epidemiology; D016017:Odds Ratio; D012042:Registries; D012680:Sensitivity and Specificity; D012907:Smoking / Q000453:epidemiology; D035501:Uncertainty; D055815:Young Adult
Misclassification; direct imputation; fully Bayesian analysis; lung cancer; sensitivity/specificity imputation; smoking status