Weighting the United States Research Program data to known population estimates using raking.

Background: The Research Program aims to collect longitudinal health-related data from a million individuals in the United States. An inherent challenge of a non-probability sampling strategy through voluntary participation in is that findings may not be nationally representative for addressing health and health care at the population level. We generated survey weights for the data that can be used to address the challenge.

Research Design: We developed raked weights using demographic, health, and socioeconomic variables available in both the 2020 National Health Interview Survey (NHIS) and . We then compared the unweighted and weighted prevalence of a set of health-related variables (health behaviors, health conditions, and health insurance coverage) estimated from data with the weighted prevalence estimates obtained from NHIS data.

Subjects: The sample included 100,391 participants 18 years of age and older with complete data collected between May 2017 and January 2022 across the United States.

Results: Final variables in the raking procedure included age, sex, race/ethnicity, region of residence, annual household income, and home ownership. The mean percentage difference between known proportions obtained from the NHIS and was reduced by 18.89% for health-related variables after applying the raked weights.

Conclusions: Raking improved the comparability of prevalence estimates obtained from to known national prevalence estimates. Refining the process of variable selection for raking may further improve the comparability between and nationally representative data.