Estimation and inference for the population attributable risk in the presence of misclassification.

Because it describes the proportion of disease cases that could be prevented if an exposure were entirely eliminated from a target population as a result of an intervention, estimation of the population attributable risk (PAR) has become an important goal of public health research. In epidemiologic studies, categorical covariates are often misclassified. We present methods for obtaining point and interval estimates of the PAR and the partial PAR (pPAR) in the presence of misclassification, filling an important existing gap in public health evaluation methods. We use a likelihood-based approach to estimate parameters in the models for the disease and for the misclassification process, under main study/internal validation study and main study/external validation study designs, and various plausible assumptions about transportability. We assessed the finite sample perf ormance of this method via a simulation study, and used it to obtain corrected point and interval estimates of the pPAR for high red meat intake and alcohol intake in relation to colorectal cancer incidence in the HPFS, where we found that the estimated pPAR for the two risk factors increased by up to 317% after correcting for bias due to misclassification.

A general approach to detect gene (G)-environment (E) additive interaction leveraging G-E independence in case-control studies.

It is increasingly of interest in statistical genetics to test for the presence of an additive interaction between genetic (G) and environmental (E) risk factors. In case-control studies involving a rare disease, a statistical test of no additive G×E interaction typically entails a test of no relative excess risk due to interaction (RERI). It has been shown that a likelihood ratio test of a null RERI incorporating the G-E independence assumption (RERI-LRT) outperforms the standard approach. The RERI-LRT relies on correct specification of a logistic model for the binary outcome, as a function of G, E, and auxiliary covariates. However, when at least one exposure is not categorical or auxiliary covariates are present, nonparametric estimation may not be feasible, while parametric logistic regression will a priori rule out the null hypothesis of no additive interaction in most practical situations, inflating type I error rate. In this paper, we present a general approach to test for G × E additive interaction exploiting G-E independence. Unlike the RERI-LRT, it allows the regression model for the binary outcome to remain unrestricted, and nonetheless still allows for covariate adjustment in order to ensure the G-E independence assumption or to rule out residual confounding. The methods are illustrated through extensive simulation studies and an ovarian cancer study.

The effect of risk factor misclassification on the partial population attributable risk.

The partial population attributable risk (pPAR) is used to quantify the population-level impact of preventive interventions in a multifactorial disease setting. In this paper, we consider the effect of nondifferential risk factor misclassification on the direction and magnitude of bias of pPAR estimands and related quantities. We found that the bias in the uncorrected pPAR depends nonlinearly and nonmonotonically on the sensitivities, specificities, relative risks, and joint prevalence of the exposure of interest and background risk factors, as well as the associations between these factors. The bias in the uncorrected pPAR is most dependent on the sensitivity of the exposure. The magnitude of bias varies over a large range, and in a small region of the parameter space determining the pPAR, the direction of bias is away from the null. In contrast, the crude PAR can only be unbiased or biased towards the null by risk factor misclassification. The semiadjusted PAR is calculated using the formula for the crude PAR but plugs in the multivariate-adjusted relative risk. Because the crude and semiadjusted PARs continue to be used in public health research, we also investigated the magnitude and direction of the bias that may arise when using these formulae instead of the pPAR. These PAR estimators and their uncorrected counterparts were calculated in a study of risk factors for colorectal cancer in the Health Professionals Follow-up Study, where it was found that because of misclassification, the pPAR for low folate intake was overestimated with a relative bias of 48%, when red meat and alcohol intake were treated as misclassified risk factors that are not modified, and when red meat was treated as the modifiable risk factor, the estimated value of the pPAR went from 14% to 60%, further illustrating the extent to which misclassification can bias estimates of the pPAR.