Measurement error model for misclassified binary responses.
The article considers regression models for binary response in a situation when the response is subject to classification error. It is also assumed that some of the covariates are unobservable, but measurements on its surrogates are available. Likelihood based analysis is developed to fit the model. A sensitivity analysis is also carried out through simulation to ascertain the effect of ignoring classification error and/or measurement error on the estimation of regression parameters. At the end, the methodology developed in this paper is illustrated through an example.
Roy
Surupa
S
Department of Statistics, St. Xavier's College, Calcutta, India.
Banerjee
T
T
Maiti
Tapabrata
T
D015331:Cohort Studies; D003198:Computer Simulation; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D009369:Neoplasms / Q000401:mortality; D011829:Radiation Dosage; D012044:Regression Analysis; D017741:Survivors