26: Hypothesis Tests for Comparing Two Independent Population Proportions with Censored Data

Music City Center 

Real-world electronic health record(EHR) data can be used to identify rare adverse events of medications due to its large sample size. This study use a large nationwide EHR COVID-19 database to identify potential adverse effects of COVID-19 vaccines by comparing to historical EHR data of influenza vaccination. To compare the proportion of adverse events in a pre-specified time interval after vaccination, the censored or incomplete data should be considered. We propose an inverse probability of censoring weight(IPCW) adjusted hypothesis test to deal with the censored EHR data problem. An asymptotic consistent estimator of the event proportion is proposed and the corresponding asymptotic distribution is derived under the data censoring. The asymptotic hypothesis test for comparing the event proportion between the two groups under censoring is established. The simulation studies demonstrate the validity of the proposed IPCW-adjusted estimate and test statistic. We show that the proposed IPCW-adjusted estimate of the event proportion can remove the estimation bias and the type I error of IPCW-adjusted test can be controlled well at the nominal level when the censoring rate increases.

Adverse effects

COVID-19 vaccine

hypothesis testing with censored data

IPCW 

Biometrics Section