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P. Gustafson
Measurement Error and Misclassification in Statistics and
Epidemiology
(MEMSE for short)
was published by
Chapman and Hall / CRC Press
in the Fall of 2003.
(It is the 13th volume in their Interdisciplinary Statistics Series.)
Check out the
publisher's webpage about the book.
Errata for MEMSE (pdf), last updated May 2, 2005.
Some R software and data used in MEMSE
is given below. (Last updated July 21, 2004.
I apologize that this is still very incomplete. I will do more updates as time permits.)
Ch. 4 Software (Adjusting for Measurement Error)
logreg1()
- MCMC for logistic regression with measurement error, supports both
normal exposure model and flexible `reverse exposure' model
(see Secs. 4.4, 4.5, 4.7 of MEMSE). Also
logreg0() fits logistic regression without measurement error.
Ch. 5 Software (Adjusting for Misclassification)
dual.ind()
- MCMC for DUAL-IND model: two imperfect exposure assessments applied to two
popluations (i.e., control and case), with assumption that the two assessments are conditionally independent given the true exposure status.
(See Sec. 5.3 of MEMSE.)
dual.dep()
- MCMC for DUAL-DEP model: two imperfect exposure assessments applied to two
popluations, without the independence assumption, but with prior distributions on the dependence parameters.
(See Sec. 5.3. of MEMSE.)
dual.ind.reg()
- MCMC for extension of DUAL-IND model to handle covariates.
Contains two functions: dual.ind.reg1() for modelling exposure given outcome and covariates, dual.ind.reg2() for modelling outcome given exposure and
covariates.
(See Sec. 5.4 of MEMSE.)
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