A Pseudo-simulation Extrapolation Method for Misspecified Models with Errors-in-variables in Epidemiological Studies

Oregon Convention Center 

In epidemiology studies, it is often of interest to consider a misspecified model, which categorizes a continuous variable to analyze the risk of obesity for a better model interpretation. When the continuous variable is contaminated with measurement errors, ignoring this issue and performing regular statistical analysis leads to severely biased point estimators and invalid confidence intervals. However, most existing methods addressing measurement errors either do not consider model misspecification or have strong parametric distributional assumptions. We propose a flexible pseudo-simulation extrapolation method, which provides valid and robust statistical inference under various models and has no distributional assumptions on the observed data. We demonstrate that the proposed method can provide unbiased point estimation and valid confidence intervals under various regression models. By analyzing the Food Frequency Questionnaire in UK Biobank data, we show that ignoring measurement errors underestimates the impact of high fat intake diet on BMI and obesity by at least 30% and 60%, respectively, compared to the results of correcting measurement errors by the proposed method.