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

This misclassification can also bias the association of risk factors with the disease condition [8,9,10]. Standard approach to handle ...

Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis · Authors · Affiliations.

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

Methods: We formulate this problem as a missing data problem because the disease status is unknown for those who do not receive the test. We ...

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 ...

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

Methods: We formulate this problem as a missing data problem because the disease status is unknown for those who do not receive the test. We propose a Bayesian ...

Accounting for Misclassified Outcomes in Binary Regression Models ...

In simulations, we used mean squared error to assess the tradeoff between bias and precision. Despite its large bias, the naive analysis had a ...

ACCOUNTING FOR MISCLASSIFICATION BIAS IN BINARY ...

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

Accounting for Misclassification Bias in Binary Outcome Measures of ...

of measures of association caused by classification errors in categorical outcome variables, particularly in health-related applications.1. This paper ...

Misclassification bias | Catalog of Bias

This can lead to incorrect associations being observed between the assigned categories and the outcomes of interest. Non-differential misclassification occurs ...

Peer Review reports - BMC Medical Research Methodology

Peer Review reports. From: Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis. Original Submission. 15 Aug ...

Accounting for Misclassification Bias in Binary Outcome Measures of ...

Accounting for Misclassification Bias in Binary Outcome Measures of Illness: The Case of Post-Traumatic Stress Disorder in Male Veterans · 9 Citations · 65 ...

Accounting for misclassification bias of binary outcomes due ... - OUCI

Riley MR, Bass NM, Rosenthal P, et al. Underdiagnosis of pediatric obesity and underscreening for fatty liver disease and metabolic syndrome by pediatricians ...

Accounting for Misclassification Bias in Binary Outcome Measures of ...

Request PDF | Accounting for Misclassification Bias in Binary Outcome Measures of Illness: The Case of Post-Traumatic Stress Disorder in Male Veterans | The ...

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

Article "Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis" Detailed information of ...

Misclassification in binary choice models - ScienceDirect.com

Misclassification of a binary variable is necessarily non-classical measurement error, and thus leads to bias. However, there are few general results on bias in ...

METHODS TO ACCOUNT FOR OUTCOME MISCLASSIFICATION IN ...

Approaches to account for bias in crude effect estimates due to use of a misclassified binary outcome variable have existed for more than half a century (44).

Statistical inference for association studies in the presence of binary ...

We consider regression models where a binary outcome variable is potentially misclassified. Misclassified binary outcomes are common in ...

Kazuki Yoshida on X: "Accounting for misclassification bias of binary ...

Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis https://t.co/J0MYI4qx3i.

Accounting for Response Misclassification and Covariate ...

Failure to account for misclassification and/or measurement error is well known to potentially bias estimates, generally towards the null hypothesis. Both are ...

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

Abstract. Methods that ignore errors in binary responses yield biased estimators of the associations of covariates with response. This paper derives genera.