IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v10y2014i1p21n5.html
   My bibliography  Save this article

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

Author

Listed:
  • Sapp Stephanie
  • 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 1-21, May.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:21:n:5
    DOI: 10.1515/ijb-2013-0009
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2013-0009
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2013-0009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Gruber Susan & van der Laan Mark J., 2012. "Targeted Minimum Loss Based Estimation of a Causal Effect on an Outcome with Known Conditional Bounds," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-18, July.
    6. 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).
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Susan Gruber & Mark J. van der Laan, 2013. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics, The International Biometric Society, vol. 69(1), pages 254-262, March.
    2. Lianming Wang & David B. Dunson, 2011. "Semiparametric Bayes' Proportional Odds Models for Current Status Data with Underreporting," Biometrics, The International Biometric Society, vol. 67(3), pages 1111-1118, September.
    3. Audrey Renson & Michael G. Hudgens & Alexander P. Keil & Paul N. Zivich & Allison E. Aiello, 2023. "Identifying and estimating effects of sustained interventions under parallel trends assumptions," Biometrics, The International Biometric Society, vol. 79(4), pages 2998-3009, December.
    4. Min Zhang & Marie Davidian, 2008. "“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data," Biometrics, The International Biometric Society, vol. 64(2), pages 567-576, June.
    5. Philipp Baumann & Enzo Rossi & Michael Schomaker, 2022. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    6. Lina M. Montoya & Michael R. Kosorok & Elvin H. Geng & Joshua Schwab & Thomas A. Odeny & Maya L. Petersen, 2023. "Efficient and robust approaches for analysis of sequential multiple assignment randomized trials: Illustration using the ADAPT‐R trial," Biometrics, The International Biometric Society, vol. 79(3), pages 2577-2591, September.
    7. Matthew Blackwell & Anton Strezhnev, 2022. "Telescope matching for reducing model dependence in the estimation of the effects of time‐varying treatments: An application to negative advertising," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 377-399, January.
    8. Kara E. Rudolph & Jonathan Levy & Mark J. van der Laan, 2021. "Transporting stochastic direct and indirect effects to new populations," Biometrics, The International Biometric Society, vol. 77(1), pages 197-211, March.
    9. David Benkeser & Keith Horvath & Cathy J. Reback & Joshua Rusow & Michael Hudgens, 2020. "Design and Analysis Considerations for a Sequentially Randomized HIV Prevention Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 446-467, December.
    10. Stephanie Chan & Xuan Wang & Ina Jazić & Sarah Peskoe & Yingye Zheng & Tianxi Cai, 2021. "Developing and evaluating risk prediction models with panel current status data," Biometrics, The International Biometric Society, vol. 77(2), pages 599-609, June.
    11. Helene C. W. Rytgaard & Frank Eriksson & Mark J. van der Laan, 2023. "Estimation of time‐specific intervention effects on continuously distributed time‐to‐event outcomes by targeted maximum likelihood estimation," Biometrics, The International Biometric Society, vol. 79(4), pages 3038-3049, December.
    12. Ying Zhang & Lei Hua & Jian Huang, 2010. "A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 338-354, June.
    13. Ronald Herrera & Ursula Berger & Ondine S. Von Ehrenstein & Iván Díaz & Stella Huber & Daniel Moraga Muñoz & Katja Radon, 2017. "Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation," IJERPH, MDPI, vol. 15(1), pages 1-15, December.
    14. Chen, Ling & Sun, Jianguo, 2010. "A multiple imputation approach to the analysis of interval-censored failure time data with the additive hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1109-1116, April.
    15. Choi, Taehwa & Kim, Arlene K.H. & Choi, Sangbum, 2021. "Semiparametric least-squares regression with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    16. Mireille E. Schnitzer & Erica E.M. Moodie & Mark J. van der Laan & Robert W. Platt & Marina B. Klein, 2014. "Modeling the impact of hepatitis C viral clearance on end-stage liver disease in an HIV co-infected cohort with targeted maximum likelihood estimation," Biometrics, The International Biometric Society, vol. 70(1), pages 144-152, March.
    17. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
    18. Youmi Suk, 2024. "A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 61-91, February.
    19. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    20. Veronica Sciannameo & Gian Paolo Fadini & Daniele Bottigliengo & Angelo Avogaro & Ileana Baldi & Dario Gregori & Paola Berchialla, 2022. "Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Mi," IJERPH, MDPI, vol. 19(22), pages 1-13, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:21:n:5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.