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Multiple Imputation of Missing Time-Dependent Covariates in Survival Analysis

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
Qingxia Chen  
Vanderbilt University Medical Center

First Author:

Xiaoming Gao  
Universitiy of North Carolina at Chapel Hill

Presenting Author:

Xiaoming Gao  
N/A

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

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