Accounting for misclassification bias of binary outcomes due to ...

Addressing Underreporting in Binary Data from Multiple Sources

(2006), this is not the case in discrete-outcome models, where measurement error is misclassification; risking not only a loss of precision but also bias in ...

Corrected ROC analysis for misclassified binary outcomes

In this paper, we study the effect of misclassification on accuracy assessment for risk prediction models and find that it leads to bias in the area under the ...

handling both selection bias and outcome misclassification

We apply these methods to data from The Michigan Genomics Initiative (MGI), a longitudinal EHR-linked biorepository. Key words: biobank, electronic health ...

Sources of Systematic Error or Bias: Information Bias

Effect of differential misclassification of exposure or health outcome ... outcomes unlikely to be related to exposure. Complications in predicting ...

Corrected ROC analysis for misclassified binary outcomes

As misclassification increases, bias increases in standard estimates of AUC (triangles). The misclassification-adjusted AUC estimators (circles) ...

Corrected ROC analysis for misclassified binary outcomes

Here, we report that using misclassified outcomes in a standard ROC analysis leads to biased AUC esti- mates. The AUC bias exists regardless of ...

Misclassification in Administrative Claims Data: Quantifying the ...

Misclassified Outcomes. In most cases, nondifferential misclassification of a binary outcome will also result in bias toward the null.

Accounting for misclassification bias of binary outcomes due to ...

Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis.在哪里下载？这篇文献在哪里可以阅读？

The Case of Post-Traumatic Stress Disorder in Male Veterans

When the outcome variable is binary, however, regression coefficients, both linear and nonlinear, will contain bias, even if the measurement error (in this ...

Probabilistic Sensitivity Analysis of Misclassification

In the instances when residual bias in adjusted model estimates is discussed by researchers, it is often qualitative in nature. For example, discussion of ...

Selection and Misclassification Biases in Longitudinal Studies

It is already recognized that misclassification of outcome or exposure during follow-up leads to bias towards null in the estimated associations (7, 12, 21) as ...

Bias Due to Misclassification in Comparative Effectiveness Research

Outcome misclassification: conventional wisdom. • Nondifferential causes bias toward the null. • Special cases: – Risk ratio unbiased if specificity=1.0.

Statistical inference for association studies using electronic health ...

Addressing misclassification and selection biases simultaneously is a more challenging problem than dealing with each on its own, and we propose ...

Binomial regression with a misclassified covariate and outcome

Misclassification involving binary variables is due to many factors, e.g. ... Bias and efficiency loss due to misclassified responses in binary regression.

Outcome Misclassification in Logistic Regression

... biases caused by misclassified outcomes, I evaluate data ... Bias and Efficiency Loss Due to Misclassified Responses in Binary Regression.

Application of a Web-based Tool for Quantitative Bias Analysis

It can be used for differential and nondifferential misclassification of exposure or outcome. There are options to conduct simple bias analysis using data (i.e. ...

Logistic Regression with Misclassification in Binary Outcome Variables

Bias and efficiency loss due to misclassified responses in binary. 615 ... Accounting for misclassification bias in binary outcome measures of. 619.

Dealing with and understanding misclassification bias - Kevin Staub

outcomes relative to the sparser case of a binary outcome. ... to the unobserved health h∗, neglecting di leads to a classi- cal omitted-variables-bias situation.

Correcting Hazard Ratio Estimates for Outcome Misclassification ...

Conclusions: MI performs well to account for bias in HR estimates due to misclassification in time-to-event outcomes when validation data are available from ...

Bias and efficiency loss due to misclassified responses in binary ...

SUMMARY Methods that ignore errors in binary responses yield biased estimators of the associations of covariates with response.