Citation

Green CL, Brownie C, Boos DD, Lu JC, Krucoff MW. Stat. Methods Med. Res. 2012; ePub(ePub): ePub.

Affiliation

1Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.

Copyright

(Copyright © 2012, SAGE Publishing)

DOI

10.1177/0962280212466089

PMID

23166160

Abstract

We propose a novel likelihood method for analyzing time-to-event data when multiple events and multiple missing data intervals are possible prior to the first observed event for a given subject. This research is motivated by data obtained from a heart monitor used to track the recovery process of subjects experiencing an acute myocardial infarction. The time to first recovery, T(1), is defined as the time when the ST-segment deviation first falls below 50% of the previous peak level. Estimation of T(1) is complicated by data gaps during monitoring and the possibility that subjects can experience more than one recovery. If gaps occur prior to the first observed event, T, the first observed recovery may not be the subject's first recovery. We propose a parametric gap likelihood function conditional on the gap locations to estimate T(1). Standard failure time methods that do not fully utilize the data are compared to the gap likelihood method by analyzing data from an actual study and by simulation. The proposed gap likelihood method is shown to be more efficient and less biased than interval censoring and more efficient than right censoring if data gaps occur early in the monitoring process or are short in duration.

Language: en