IJE Advance Access published online on September 9, 2009
International Journal of Epidemiology, doi:10.1093/ije/dyp278
Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods
Department of Epidemiology and Department of Statistics, University of California, Los Angeles, CA 90095-1772, USA. E-mail: lesdomes{at}ucla.edu
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Abstract |
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I present some extensions of Bayesian methods to situations in which biases are of concern. First, a basic misclassification problem is illustrated using data from a study of sudden infant death syndrome. Bayesian analyses are then given. These analyses can be conducted directly, or by converting actual-data records to incomplete records and prior distributions to complete-data records, then applying missing-data techniques to the augmented data set. The analyses can easily incorporate any complete (‘validation’ or second-stage) data that might be available, as well as adjustments for confounding and selection bias. The approach illustrates how conventional analyses depend on implicit certainty that bias parameters are null and how these implausible assumptions can be replaced by plausible priors for bias parameters.
Keywords Bayesian methods, bias, biostatistics, epidemiology, missing data, observational studies, odds ratio, relative risk, risk analysis, risk assessment, sensitivity analysis, validation
Accepted 30 June 2009