Improving External Validity of Epidemiologic Analyses by Incorporating Data from Population-Based Surveys

Abstract

Many epidemiologic studies forgo probability sampling and turn to volunteer-based samples because of cost, confidentiality, response burden, and invasiveness of biological samples. However, the volunteers may not represent the underlying target population mainly due to self-selection bias. Therefore, standard epidemiologic analyses may not be generalizable to the target population, which is called lack of “external validity.” In survey research, propensity score (PS)-based approaches have been developed to improve representativeness of the nonprobability samples by using population-based surveys as references. These approaches create a set of “pseudo-weights” to weight the nonprobability sample up to the target population. There are two main types of PS-based approaches: (1) PS-based weighting methods using PSs to estimate participation rates of the nonprobability sample; for example, the inverse of PS weighting (IPSW); (2) PS-based matching methods using PSs to measure similarity between the units in the nonprobability sample and the reference survey sample, such as PS adjustment by subclassification (PSAS). Although the PS-based weighting methods reduce the bias, they are sensitive to propensity model misspecification and can be inefficient. The PS-based matching methods are more robust to the propensity model misspecification and can avoid extreme weights. However, matching methods such as PSAS are less effective at bias reduction. This dissertation proposes a novel PS-based matching method, named the kernel weighting (KW) approach, to improve the external validity of epidemiologic analyses that gain a better bias–variance tradeoff. A unifying framework is established for PS-based methods to provide three advances. First, the KW method is proved to provide consistent estimates, yet generally has smaller mean-square error than the IPSW. Second, the framework reveals a fundamental strong exchangeability assumption (SEA) underlying the existing PS-based matching methods that has previously been unknown. The SEA is relaxed to a weak exchangeability assumption that is more realistic for data analysis. Third, survey weights are scaled in propensity estimation to reduce the variance of the estimated PS and improve efficiency of all PS-based methods under the framework. The performance of the proposed PS-based methods is evaluated for estimating prevalence of diseases and associations between risk factors and disease in the finite population.