IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v69y2013i1p254-262.html
   My bibliography  Save this article

An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data

Author

Listed:
  • Susan Gruber
  • Mark J. van der Laan

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:1:p:254-262
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01829.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    2. Porter Kristin E. & Gruber Susan & van der Laan Mark J. & Sekhon Jasjeet S., 2011. "The Relative Performance of Targeted Maximum Likelihood Estimators," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, August.
    3. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    4. Gruber Susan & van der Laan Mark J., 2010. "A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-18, August.
    5. 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).
    6. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    7. 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.
    8. Laan Mark J. van der & Dudoit Sandrine & Vaart Aad W. van der, 2006. "The cross-validated adaptive epsilon-net estimator," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 1-23, December.
    9. 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.
    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. 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.
    2. 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.
    3. Stitelman Ori M & Wester C. William & De Gruttola Victor & van der Laan Mark J., 2011. "Targeted Maximum Likelihood Estimation of Effect Modification Parameters in Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, March.
    4. 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.
    5. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
    6. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    7. Zhiwei Zhang & Zhen Chen & James F. Troendle & Jun Zhang, 2012. "Causal Inference on Quantiles with an Obstetric Application," Biometrics, The International Biometric Society, vol. 68(3), pages 697-706, September.
    8. 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.
    9. 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.
    10. 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.
    11. Sherri Rose & Sharon‐Lise Normand, 2019. "Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug‐eluting coronary artery stents," Biometrics, The International Biometric Society, vol. 75(1), pages 289-296, March.
    12. Jie Zhou & Zhiwei Zhang & Zhaohai Li & Jun Zhang, 2015. "Coarsened Propensity Scores and Hybrid Estimators for Missing Data and Causal Inference," International Statistical Review, International Statistical Institute, vol. 83(3), pages 449-471, December.
    13. 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.
    14. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
    15. Wei, Kecheng & Qin, Guoyou & Zhang, Jiajia & Sui, Xuemei, 2022. "Doubly robust estimation in causal inference with missing outcomes: With an application to the Aerobics Center Longitudinal Study," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    16. Chaffee Paul H. & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation for Dynamic Treatment Regimes in Sequentially Randomized Controlled Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-32, June.
    17. Zhiwei Zhang & Richard M. Kotz & Chenguang Wang & Shiling Ruan & Martin Ho, 2013. "A Causal Model for Joint Evaluation of Placebo and Treatment-Specific Effects in Clinical Trials," Biometrics, The International Biometric Society, vol. 69(2), pages 318-327, June.
    18. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    19. Stitelman Ori M. & De Gruttola Victor & van der Laan Mark J., 2012. "A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-39, September.
    20. 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.

    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:bla:biomet:v:69:y:2013:i:1:p:254-262. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.