Interrupted time series methods for non-random sampling study designs with known sampling weights

Music City Center 

The University of California Irvine Consent-to-Contact (C2C) registry initiated an interrupted time series (ITS) design recruitment strategy study (C2C-RSS) to assess the effectiveness of interventions in recruiting individuals from disadvantaged neighborhoods in Orange County, California. To define disadvantage, we utilized the Area Deprivation Index (ADI) (Kind and Buckingham, 2018). The C2C-RSS aims to estimate a marginal intervention effect across ADI deciles on recruitment and assess effect modification by ADI strata. We employed a non-random sampling design to ensure uniform inclusion across ADI deciles. To adjust for sampling bias, we extend the Robust-Multiple ITS model (Cruz et al., 2019) to incorporate inverse probability of known sampling weights in estimating a marginal mean function. We additionally propose two variance estimators: the first quantifies uncertainty of the unknown change point associated with the intervention and the second additionally accounts for misspecification of the mean model. We demonstrate the performance of our methods through empirical simulation studies. We further use our proposed methods to assess power to achieve the aims of the C2C-RSS.

interrupted time series

intervention assessment

multiple units


change point variability

sampling weights 

Biometrics Section