Much 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, 1520-1539.
  • Gustafson, P. and Greenland, S. Misclassification. Chapter submitted for Handbook of Epidemiology, 2nd. Ed. Springer, May 2010

while the notion of nearly-nondifferential misclassification is described more fully in:

  • Chu, R., Gustafson, P., Le, N. (2010). Bayesian adjustment for exposure misclassification. Statistics in Medicine, 29, 994-1003.

References on Bayesian methods for prevalence estimation or evaluation of a new diagnostic test in the absence of a gold-standard test

  • Joseph L, Gyorkos TW and Coupal L. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold-standard American Journal of Epidemiology. 141(3): 263-72. 1995
  • Dendukuri N, Joseph L. Bayesian approaches to modeling the conditional dependence between diagnostic tests. Biometrics, 57(1):158-67. 2001
  • Hadgu A, Dendukuri N, Hilden J. The Evaluation of Nucleic Acid Amplification Tests in the Absence of a Perfect Gold-Standard Test: A Review of the Epidemiological and Statistical Issues. Epidemiology, 16(5):604-612. 2005
  • Pai M, Dendukuri N, Wang L, Joshi R, Kalantri S and Rieder H. Improving the estimation of tuberculosis infection prevalence using T-cell-based assay and mixture models. International Journal of Tuberculosis and Lung Disease, 12(8):895-902. 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, 47-54.
  • Greenland (2005). Multiple bias modeling for analysis of observational data (with discussion). Journal of the Royal Statistical Society, Series A 168, 267-308.
  • 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.). Lippincott-Wolters-Kluwer, Philadelphia, 345-380.
  • 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 1-58488-388-X
  • Spiegelhalter D, Abrams KR and Myles JP. (2004) Bayesian Approaches to Clinical Trials and Health-Care Evaluation D. J. 2004 John Wiley & Sons, Ltd ISBN 0-471-49975-7

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, 111-140.
  • Gustafson, P. "Bayesian inference for partially identified models." International Journal of Biostatistics 6 (issue 2, article 17).