Accounting for Misclassified Outcomes in Binary Regression Models ...

Bayesian analysis for matrix-variate logistic regression with/without ...

However, inference based on such a model is impaired by the presence of the response misclassification and spurious covariates It is imperative ...

accounting for misclassification bias in binary outcome measures of ...

at least for the classical model: purely random errors will lead to a loss of efficiency but not to bias in regression coefficients. When the outcome ...

Identification and Estimation of Regression Models with ...

the effect of unions on wages taking misclassification of union status explicitly into account ... (2003): “Misclassified Regressors in Binary Choice Models ...

Accounting for Measurement Error and Misclassification in Variables ...

effect model, so we only reported the fixed-effect regression. The results ... Second, corrections for Linear and Logit models appear to be more ...

Regression with a Misclassified Binary Regressor: Correcting for the ...

The fact the binary regression model and the outcome equation have the same set of regressors Xi is not especially problematic because of the nonlinearity ...

Correcting for bias due to misclassification when error-prone ...

These are discussed in the context of a more general form for the exposure-outcome association, which includes the logistic model. 2.3.1 Regression calibration ...

Correcting for Misclassified Binary Regressors Using Instrumental ...

The. OLS regression of an outcome on a misclassified binary regressor yields a corresponding lower bound (Aigner 1973;. Bollinger 1996). 4DiTraglia and Garcia- ...

Likelihood Inference in the Random Effects Logistic Regression ...

‎Insensitivity of errors in either covariate or response variable is‎, ‎not always plausible‎. ‎With nonlinear regression models for the outcome process‎, ‎ ...

A Bayesian Approach to Account for Misclassification and ... - MDPI

We extend previous work on Poisson regression models that allow for misclassification by thoroughly discussing the basis for the models and ...

Regression with a Misclassified Binary Regressor: Correcting for the ...

The fact the binary regression model and the outcome equation have the same set of regressors Xi is not especially problematic because of the nonlinearity ...

handling both selection bias and outcome misclassification

Bias and Efficiency Loss Due to Misclassified Responses in. Binary Regression. ... Figure 2: Flowchart of data analysis accounting for both misclassification ...

Accounting for Misclassified Outcomes in Binary Regression Models ...

Accounting for Misclassified Outcomes in Binary Regression Models Using Multiple Imputation With Internal Validation Data 文献详情，提供最新的国际国内科学 ...

Comparing methods of misclassification correction for studies of ...

methods for logistic regression analysis of two-stage data. Stat Med. 1997 ... A Bayesian approach to prospective binary outcome studies with.

Assessing robustness of hazard ratio estimates to outcome ... - OUCI

JK Edwards, Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation data, American Journal of ...

A Bayesian model for misclassified binary outcomes and correlated ...

Breast cancer patients may experience ipsilateral breast tumor relapse (IBTR) after breast conservation therapy. IBTR is classified as either true local ...

IDENTIFICATION OF REGRESSION MODELS WITH A ...

The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the ...

Misclassification in Binary Choice Models with Sample Selection

In the case of binary choice models, we assume that also the dependent variable of the outcome equation is binary. The likelihood function takes into account ...

Flexibly Accounting for Exposure Misclassification With External ...

Here, we present a reparameterized imputation approach for measurement error (RIME) that can be used with internal or external validation data. We illustrate ...

A weighting method for simultaneous adjustment for confounding ...

proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. ... logistic regression models of A and Y on (Z, B, ...

(PDF) Bayesian approach to average power calculations for binary ...

Bayesian approach to average power calculations for binary regression models with misclassified outcomes. ... accounts for possible misclassification of the ...