Correcting for Biases Arising in Epidemiologic Research

Abstract

In chapter 1, we explore the performance of naive least squares estimators for latency parameters in linear models in the presence of measurement error. We prove that in many scenarios under a general measurement error setting, the least squares estimator for the latency parameter remains consistent, while the regression coefficient estimates are inconsistent as has previously been found in standard measurement error models where the primary disease model does not involve a latency parameter. Conditions under which this result holds are generalized to a wide class of covariance structures and mean functions. The findings are illustrated in a study of body mass index in relation to physical activity in the Health Professionals Follow-up Study. In chapter 2, we extend the results obtained in chapter 1 to the survival setting when the exposure of interest is a time-varying recent-moving cumulative average. We show that when the disease outcome is rare, the latency parameter for a surrogate exposure is approximately the same as the latency parameter for the corresponding true exposure. We show these results in a series of simulations and illustrate the findings in a study of air pollution and incidence lung cancer in the Nurses Health Study. In chapter 3, we specificy a statistical framework for estimation and inference based on inverse probability weighting (IPW) to adjust for selection bias in EHR-based research that allows for a hierarchy of missingness mechanisms to better align with the complex nature of electronic health record (EHR) data. We show that this estimator is consistent and asymptotically Normal, and we derive the form of the asymptotic variance. We use simulations to highlight the potential for bias in EHR studies when standard approaches are used to account for selection bias. We use this approach to adjust for selection in an on-going, multi-site EHR-based study of bariatric surgery on BMI.