Journal ArticleDOI
Measurement error model for misclassified binary responses
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TLDR
The article considers regression models for binary response in a situation when the response is subject to classification error and it is assumed that some of the covariates are unobservable, but measurements on its surrogates are available.Abstract:
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.read more
Citations
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Violations of Assumptions in School-Based Single-Case Data: Implications for the Selection and Interpretation of Effect Sizes.
TL;DR: Significant violations were found in total and across fields, including low levels of autocorrelation and moderate levels of absolute trend, which affect the selection and interpretation of ESs at the individual study level and for meta-analysis.
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A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression.
TL;DR: A new Bayesian Poisson regression procedure is derived that accounts and corrects for misclassification for a count variable with two categories that is operationally effective to correct and account for mis classification effects in Poisson count regression models.
Journal ArticleDOI
Bayesian model selection for logistic regression with misclassified outcomes
Richard Gerlach,James D. Stamey +1 more
TL;DR: In this article, the problem of variable selection for logistic regression when the dependent variable is measured imperfectly, under both differential and non-differential misclassification, is considered.
Journal ArticleDOI
Binary Regression with Misclassified Response and Covariate Subject to Measurement Error : a Bayesian Approach
TL;DR: A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response, and no model parameters need be assumed known.
Journal ArticleDOI
Gauge capability studies for attribute data
JrJung Lyu,Ming Nan Chen +1 more
TL;DR: A gauge repeatability and reproducibility (R&R) method is developed to assess the capability of a measurement system for attribute data and integrates the iterative weighted least squares method and deviance analysis.
References
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Book
Measurement Error in Nonlinear Models
TL;DR: In this paper, the authors propose fitting methods and models for regression and attenuation in the context of Bayesian methods and nonparametric regression for density estimation and non-parametric regression.
Journal ArticleDOI
Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error
TL;DR: Two methods are provided to correct relative risk estimates obtained from logistic regression models for measurement errors in continuous exposures within cohort studies that may be due to either random (unbiased) within-person variation or to systematic errors for individual subjects.
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Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error
TL;DR: The results indicate that the failure to find a substantial positive association between breast cancer risk and saturated fat intake cannot be explained by measurement error in fat, calories, or alcohol.
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Studies of the mortality of A-bomb survivors. 9. Mortality, 1950-1985: Part 2. Cancer mortality based on the recently revised doses (DS86).
TL;DR: The present analysis still supports, in the main, estimation of lifetime risk based on the assumption of a constant relative risk among the 76,000 A-bomb survivors within the LSS sample for whom DS86 doses have been estimated, with the emphasis on biological issues associated with radiation carcinogenesis.
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On errors-in-variables for binary regression models
TL;DR: In this paper, the authors consider binary regression models when some of the predictors are measured with error and show that if the measurement error is large, the usual estimate of the probability of the event in question can be substantially in error, especially for high risk groups.