Abstract Number:
1975
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Lili Liu (1), Mohsen Rezapour (2), Vahed Maroufy (3), Yashar Talebi (3), Guo-qiang Zhang (4), Hulin Wu (5)
Institutions:
(1) N/A, N/A, (2) 1Department of Biostatistics and Data Science, School of Public Health, UT Health Houston, N/A, (3) Department of Biostatistics and Data Science, School of Public Health, UTHealth Houston, N/A, (4) Texas Institute for Restorative Neuro-technologies (TIRN), UTHealth Houston, N/A, (5) University of Texas Health Science Center At Houston, N/A
Co-Author(s):
Mohsen Rezapour
1Department of Biostatistics and Data Science, School of Public Health, UT Health Houston
Vahed Maroufy
Department of Biostatistics and Data Science, School of Public Health, UTHealth Houston
Yashar Talebi
Department of Biostatistics and Data Science, School of Public Health, UTHealth Houston
Guo-qiang Zhang
Texas Institute for Restorative Neuro-technologies (TIRN), UTHealth Houston
Hulin Wu
University of Texas Health Science Center At Houston
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Adverse effects|COVID-19 vaccine|hypothesis testing with censored data|IPCW| |
Sponsors:
Biometrics Section
Tracks:
Longitudinal/Correlated Data
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