Maximum likelihood estimation of time to first event in the presence of data gaps and multiple events.

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, T1, is defined as the time when the ST-segment deviation first falls below 50% of the previous peak level. Estimation of T1 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 T1 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.

journal_name

Stat Methods Med Res

authors

Green CL,Brownie C,Boos DD,Lu JC,Krucoff MW

doi

10.1177/0962280212466089

subject

Has Abstract

pub_date

2016-04-01 00:00:00

pages

775-92

issue

2

eissn

0962-2802

issn

1477-0334

pii

0962280212466089

journal_volume

25

pub_type

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