Abstract Number:
1926
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Xiaoming Gao (1), Fei Zou (2), Qingxia Chen (3)
Institutions:
(1) Universitiy of North Carolina at Chapel Hill, N/A, (2) University of North Carolina, N/A, (3) Vanderbilt University Medical Center, N/A
Co-Author(s):
Fei Zou
University of North Carolina
First Author:
Presenting Author:
Abstract Text:
Multiple imputation is one of the most common approaches to analyze incomplete data. Additional challenges arise when covariates are collected longitudinally, and the outcome of interest is interval-censored event time. A complete case analysis will result in reduced efficiency and could even generate biased parameter estimates. This paper proposes a time-sequential method based on the idea of fully conditional specification to multiply impute missing time-dependent covariates in studies with interval-censored outcomes. In addition to covariates of interest, the imputation model includes cumulative hazard estimated iteratively, risk status, and observance of failure in subjects' subsequent visits. Extensive simulation studies demonstrated improved performance over existing methods with reduced bias, improved efficiency, and valid inference. The proposed method was applied to the Atherosclerosis Risk in Communities (ARIC) Study to evaluate the risk of hypertension with potential predictors.
Keywords:
Cohort studies|Fully conditional specification|Interval-censored|Missing data|Time-sequential|
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
Yes
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand