BibliographyMuch of the material in Paul Gustafson's presentations is based on: Gustafson, P. and McCandless, L. (2010). Probabilistic approaches to better quantifying the results of epidemiologic studies. International Journal of Environmental Research and Public Health 7, 15201539.
 Gustafson, P. and Greenland, S. Misclassification. Chapter submitted for Handbook of Epidemiology, 2nd. Ed. Springer, May 2010
while the notion of nearlynondifferential misclassification is described more fully in: Chu, R., Gustafson, P., Le, N. (2010). Bayesian adjustment for exposure misclassification. Statistics in Medicine, 29, 9941003.
References on Bayesian methods for prevalence estimation or evaluation of a new diagnostic test in the absence of a goldstandard test Joseph L, Gyorkos TW and Coupal L. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a goldstandard American Journal of Epidemiology. 141(3): 26372. 1995
 Dendukuri N, Joseph L. Bayesian approaches to modeling the conditional dependence between diagnostic tests. Biometrics, 57(1):15867. 2001
 Hadgu A, Dendukuri N, Hilden J. The Evaluation of Nucleic Acid Amplification Tests in the Absence of a Perfect GoldStandard Test: A Review of the Epidemiological and Statistical Issues. Epidemiology, 16(5):604612. 2005
 Pai M, Dendukuri N, Wang L, Joshi R, Kalantri S and Rieder H. Improving the estimation of tuberculosis infection prevalence using Tcellbased assay and mixture models. International Journal of Tuberculosis and Lung Disease, 12(8):895902. 2008
Important references concerning bias analysis generally and Monte Carlo sensitivity analysis specifically include: Gustafson P. (2003) Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. , Chapman & Hall/CRC, Boca Raton, ISBN 9781584883357
 Greenland (2003). 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. Journal of the American Statistical Association 97, 4754.
 Greenland (2005). Multiple bias modeling for analysis of observational data (with discussion). Journal of the Royal Statistical Society, Series A 168, 267308.
 Greenland, S. and Lash, T. L. (2008). Bias analysis. Ch. 19 in: Modern Epidemiology, 3rd ed. (K. J. Rothman, S. Greenland and T. L. Lash, eds.). LippincottWoltersKluwer, Philadelphia, 345380.
 Lash, T.L., Fox, M.P., and Fink, A.K. (2009). Applying quantitative bias analysis to epidemiologic data. New York: Springer.
Texts on Bayesian statistics: Andrew G, Carlin JB, Stern HS and Rubin DB. (2003) Bayesian Data Analysis (2nd edition), Chapman & Hall/CRC, Boca Raton, ISBN 158488388X
 Spiegelhalter D, Abrams KR and Myles JP. (2004) Bayesian Approaches to Clinical Trials and HealthCare Evaluation D. J. 2004 John Wiley & Sons, Ltd ISBN 0471499757
On a more technical front, entry points to the literature on Bayesian inference in partially identified models include: Gustafson, P. (2005). On model expansion, model contraction, identifiability, and prior information: two illustrative scenarios involving mismeasured variables" (with discussion). Statistical Science 20, 111140.
 Gustafson, P. "Bayesian inference for partially identified models." International Journal of Biostatistics 6 (issue 2, article 17).
