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

Author

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  • Sapp Stephanie

    (Department of Statistics, University of California – Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA)

  • van der Laan Mark J.

    (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)

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.

Suggested Citation

  • Sapp Stephanie & van der Laan Mark J. & Page Kimberly, 2014. "Targeted Estimation of Binary Variable Importance Measures with Interval-Censored Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 77-97, May.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:21:n:5
    DOI: 10.1515/ijb-2013-0009
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    References listed on IDEAS

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    1. Peter Bacchetti & Christopher Quale, 2002. "Generalized Additive Models with Interval-Censored Data and Time-Varying Covariates: Application to Human Immunodeficiency Virus Infection in Hemophiliacs," Biometrics, The International Biometric Society, vol. 58(2), pages 443-447, June.
    2. Tianxi Cai & Rebecca A. Betensky, 2003. "Hazard Regression for Interval-Censored Data with Penalized Spline," Biometrics, The International Biometric Society, vol. 59(3), pages 570-579, September.
    3. Gruber, Susan & Laan, Mark van der, 2012. "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i13).
    4. Lu Tian & Tianxi Cai, 2006. "On the accelerated failure time model for current status and interval censored data," Biometrika, Biometrika Trust, vol. 93(2), pages 329-342, June.
    5. Daniel Rabinowitz & Rebecca A. Betensky & Anastasios A. Tsiatis, 2000. "Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data," Biometrics, The International Biometric Society, vol. 56(2), pages 511-518, June.
    6. van der Laan Mark J. & Gruber Susan, 2012. "Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-41, May.
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