IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v66y2010i1p131-139.html
   My bibliography  Save this article

Structural Nested Mean Models for Assessing Time-Varying Effect Moderation

Author

Listed:
  • Daniel Almirall
  • Thomas Ten Have
  • Susan A. Murphy

Abstract

No abstract is available for this item.

Suggested Citation

  • Daniel Almirall & Thomas Ten Have & Susan A. Murphy, 2010. "Structural Nested Mean Models for Assessing Time-Varying Effect Moderation," Biometrics, The International Biometric Society, vol. 66(1), pages 131-139, March.
  • Handle: RePEc:bla:biomet:v:66:y:2010:i:1:p:131-139
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2009.01238.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. S. Vansteelandt & E. Goetghebeur, 2003. "Causal inference with generalized structural mean models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 817-835, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wodtke, Geoffrey & Zhou, Xiang, 2019. "Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-residuals Approach," SocArXiv 86d2k, Center for Open Science.
    2. Changhui Kang & Myoung-jae Lee, 2014. "Estimation of Binary Response Models With Endogenous Regressors," Pacific Economic Review, Wiley Blackwell, vol. 19(4), pages 502-530, October.
    3. Myoung‐jae Lee & Young‐sook Kim, 2012. "Zero‐Inflated Endogenous Count In Censored Model: Effects Of Informal Family Care On Formal Health Care," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1119-1133, September.
    4. Geoffrey T. Wodtke, 2020. "Regression-based Adjustment for Time-varying Confounders," Sociological Methods & Research, , vol. 49(4), pages 906-946, November.
    5. Geoffrey T. Wodtke & Zahide Alaca & Xiang Zhou, 2020. "Regression‐with‐residuals estimation of marginal effects: a method of adjusting for treatment‐induced confounders that may also be effect modifiers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 311-332, January.
    6. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

    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. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    2. Luca Zanin & Rosalba Radice & Giampiero Marra, 2013. "Estimating the Effect of Perceived Risk of Crime on Social Trust in the Presence of Endogeneity Bias," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 523-547, November.
    3. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Aug 2024.
    4. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    5. Rosalba Radice & Luca Zanin & Giampiero Marra, 2013. "On the effect of obesity on employment in the presence of observed and unobserved confounding," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 436-455, November.
    6. Sylvie Goetgeluk & Stijn Vansteelandt, 2008. "Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data," Biometrics, The International Biometric Society, vol. 64(3), pages 772-780, September.
    7. Luke Keele & Dylan Small & Richard Grieve, 2017. "Randomization-based instrumental variables methods for binary outcomes with an application to the ‘IMPROVE’ trial," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 569-586, February.
    8. Paul Clarke & Frank Windmeijer, 2009. "Identification of Causal Effects on Binary Outcomes Using Structural Mean Models," The Centre for Market and Public Organisation 09/217, The Centre for Market and Public Organisation, University of Bristol, UK.
    9. Paul S. Clarke & Frank Windmeijer, 2012. "Instrumental Variable Estimators for Binary Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1638-1652, December.
    10. Paul S. Clarke & Tom M. Palmer & Frank Windmeijer, 2011. "Estimating structural mean models with multiple instrumental variables using the generalised method of moments," CeMMAP working papers CWP28/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Linbo Wang & Xiang Meng & Thomas S. Richardson & James M. Robins, 2023. "Coherent modeling of longitudinal causal effects on binary outcomes," Biometrics, The International Biometric Society, vol. 79(2), pages 775-787, June.
    12. Ditte Nørbo Sørensen & Torben Martinussen & Eric Tchetgen Tchetgen, 2019. "A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 639-659, October.
    13. He Jiwei & Stephens-Shields Alisa & Joffe Marshall, 2015. "Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 203-222, November.
    14. Geneletti, Sara & Baio, Gianluca & O'Keeffe, Aidan & Ricciardi, Federico, 2019. "Bayesian modelling for binary outcomes in the regression discontinuity design," LSE Research Online Documents on Economics 100096, London School of Economics and Political Science, LSE Library.
    15. 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.
    16. Bo Wei & Limin Peng & Mei‐Jie Zhang & Jason P. Fine, 2021. "Estimation of causal quantile effects with a binary instrumental variable and censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 559-578, July.
    17. Mark van der Laan & Alan Hubbard & Nicholas Jewell, 2004. "Estimation of Treatment Effects in Randomized Trials with Noncompliance and a Dichotomous Outcome," U.C. Berkeley Division of Biostatistics Working Paper Series 1157, Berkeley Electronic Press.
    18. Shinohara Russell T. & Frangakis Constantine E. & Platz Elizabeth & Tsilidis Konstantinos, 2012. "Designs Combining Instrumental Variables with Case-Control: Estimating Principal Strata Causal Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-21, January.
    19. Murielle Bochud & Valentin Rousson, 2010. "Usefulness of Mendelian Randomization in Observational Epidemiology," IJERPH, MDPI, vol. 7(3), pages 1-18, February.
    20. Mariam O. Adeleke & Gianluca Baio & Aidan G. O'Keeffe, 2022. "Regression discontinuity designs for time‐to‐event outcomes: An approach using accelerated failure time models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1216-1246, July.

    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:66:y:2010:i:1:p:131-139. 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.