  The Consent-to-Contact (C2C) registry at the University of California, Irvine
collects data from community participants to aid in the recruitment to clinical
research studies. Self-selection into the C2C likely leads to bias due in part
to enrollees having more years of education relative to the US general
population. Salazar et al. (2020) recently used the C2C to examine associations
of race/ethnicity with participant willingness to be contacted about research
studies. To address questions about generalizability of estimated associations
we estimate propensity for self-selection into the convenience sample weights
using data from the National Health and Nutrition Examination Survey (NHANES).
We create a combined dataset of C2C and NHANES subjects and compare different
approaches (logistic regression, covariate balancing propensity score, entropy
balancing, and random forest) for estimating the probability of membership in
C2C relative to NHANES. We propose methods to estimate the variance of
parameter estimates that account for uncertainty that arises from estimating
propensity weights. Simulation studies explore the impact of propensity weight
estimation on uncertainty. We demonstrate the approach by repeating the
analysis by Salazar et al. with the deduced propensity weights for the C2C
subjects and contrast the results of the two analyses. This method can be
implemented using our estweight package in R available on GitHub.
