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Many epidemiologic studies forgo probability sampling and turn tononprobability volunteer-based samples because of cost, response burden, andinvasiveness of biological samples. However, finite population inference isdifficult to make from the nonprobability samples due to the lack of populationrepresentativeness. Aiming for making inferences at the population level usingnonprobability samples, various inverse propensity score weighting (IPSW)methods have been studied with the propensity defined by the participation rateof population units in the nonprobability sample. In this paper, we propose anadjusted logistic propensity weighting (ALP) method to estimate theparticipation rates for nonprobability sample units. Compared to existing IPSWmethods, the proposed ALP method is easy to implement by ready-to-use softwarewhile producing approximately unbiased estimators for population quantitiesregardless of the nonprobability sample rate. The efficiency of the ALPestimator can be further improved by scaling the survey sample weights inpropensity estimation. Taylor linearization variance estimators are proposedfor ALP estimators of finite population means that account for all sources ofvariability. The proposed ALP methods are evaluated numerically via simulationstudies and empirically using the na\"ive unweighted National Health andNutrition Examination Survey III sample, while taking the 1997 National HealthInterview Survey as the reference, to estimate the 15-year mortality rates.

Adjusted Logistic Propensity Weighting Methods for Population Inference using Nonprobability Volunteer-Based Epidemiologic Cohorts