Model†based bootstrapping when correcting for measurement error with application to logistic regression

John P. Buonaccorsi, Giovanni Romeo and Magne Thoresen

Biometrics, 2018, vol. 74, issue 1, 135-144

Abstract: When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non†linear models, including logistic regression. Model†based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model†based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model†based bootstrap methods, as well as other standard methods. While not always perfect, the model†based approaches offer some distinct improvements over the other methods.

Date: 2018
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