Targeted Estimation of Binary Variable Importance Measures with Interval-Censored Outcomes

Sapp Stephanie (), J. van der Laan Mark () and Page Kimberly ()
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Sapp Stephanie: Department of Statistics, University of California – Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA
J. van der Laan Mark: Department of Statistics, University of California – Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA
Page Kimberly: Department of Epidemiology and Biostatistics, University of California – San Francisco, San Francisco, CA, USA

The International Journal of Biostatistics, 2014, vol. 10, issue 1, 77-97

Abstract: In most experimental and observational studies, participants are not followed in continuous time. Instead, data is collected about participants only at certain monitoring times. These monitoring times are random and often participant specific. As a result, outcomes are only known up to random time intervals, resulting in interval-censored data. In contrast, when estimating variable importance measures on interval-censored outcomes, practitioners often ignore the presence of interval censoring, and instead treat the data as continuous or right-censored, applying ad hoc approaches to mask the true interval censoring. In this article, we describe targeted minimum loss–based estimation (TMLE) methods tailored for estimation of binary variable importance measures with interval-censored outcomes. We demonstrate the performance of the interval-censored TMLE procedure through simulation studies and apply the method to analyze the effects of a variety of variables on spontaneous hepatitis C virus clearance among injection drug users, using data from the “International Collaboration of Incident HIV and HCV in Injecting Cohorts” project.

Keywords: interval censoring; missing data; observational data; targeted learning; variable importance (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/ijb-2013-0009

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