Innovative Approaches to Address Bias and Measurement Error in Epidemiological Research

Epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long-term exposure to PM2.5, in relation to cognitive decline in the Nurses' Health Study (Weuve et al., 2012). Previously, it was found the two-year decline in the standard measure of cognition was 0.018 (95% CI, -0.034 to -0.001) units worse per 10 μg/m3 increase in PM2.5 exposure. After correction, the estimated impact of PM2.5 on cognitive decline increased to 0.027 (95% CI, -0.059 to 0.005) units lower per 10 μg/m3 increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, -0.047 to -0.040) units per one year older after applying our correction method. 

A time-to-event analysis is advocated for examining associations between time-varying environmental exposures and preterm birth in cohort studies. While the identification of preterm birth entirely depends on gestational age, the true gestational age is rarely known in practice. Obstetric estimate (OE) and gestational age based on the date of last menstrual period (LMP) are two commonly used measurements, but both suffer from various sources of error. Uncertainties in gestational age result in both outcome misclassification and measurement error of time-varying exposures which can potentially introduce serious bias in health effect estimates. Motivated by the lack of validation data in large population-based studies, we develop a hierarchical Bayesian model that utilizes the two error-prone gestational age estimates to examine time-varying exposures on the risk of preterm birth while accounting for uncertainties in the estimates. The proposed approach introduces two discrete-time hazard models for the latent true gestational ages that are preterm (<37 weeks) or term (≥ 37 weeks). Then two multinomial models are adopted for characterizing misclassifications resulting from using OE-based and LMP-based gestational age. The proposed modeling framework permits the joint estimation of preterm birth risk factors and parameters characterizing gestational age misclassifications without validation data. We apply the proposed method to a birth cohort based on birth records from Kansas in 2010. Our analysis finds robust positive associations between exposure to ozone during the third trimester of pregnancy and preterm birth even after accounting for gestational age uncertainty. 

Assessing diet and physical activity in free-living populations is prone to subject to substantial measurement error. Numerous statistical methods have been developed to adjust relative risk estimates for cancer and other chronic diseases to account for bias due to measurement error in long-term dietary and physical activity data. One widely used method for this adjustment is regression calibration, which involves estimating a de-attenuation factor. In this work, we develop semi-parametric generalized method of moments estimators for the de-attenuation factor and other quantities of interest, including the correlation of each surrogate measure with the unobserved truth and intra-class correlation coefficients characterizing the random within-person variation around each measurement. This method relies only on assumptions about the first two moments of the multivariate distribution of the measures. A robust variance is derived to enable asymptotic inference. The performance of the proposed method has been evaluated in extensive simulation studies and data analysis in the Harvard Women's Lifestyle Validation Study. 

Confounding and exposure measurement error can introduce bias when drawing inference about the marginal effect of an exposure on an outcome of interest. While there are broad methodologies for addressing each source of bias individually, confounding and exposure measurement error frequently co-occur, and there is a need for methods that address them simultaneously. In this talk, corrected score methods are introduced under classical additive measurement error to draw inference about marginal exposure effects using only measured variables. Three estimators are proposed based on g-formula, inverse probability weighting, and doubly-robust estimation techniques. The estimators are shown to be consistent and asymptotically normal, and the doubly-robust estimator is shown to exhibit its namesake property. The methods, which are implemented in the R package mismex, perform well in finite samples under both confounding and measurement error as demonstrated by simulation studies. The proposed doubly-robust estimator is applied to study the effects of two biomarkers on HIV-1 infection using data from the HVTN 505 preventative vaccine trial.