IDEAS home Printed from https://ideas.repec.org/p/bep/ucbbio/1159.html
   My bibliography  Save this paper

Data Adaptive Estimation of the Treatment Specific Mean

Author

Listed:
  • Yue Wang

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)

  • Mark van der Laan

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)

Abstract

An important problem in epidemiology and medical research is the estimation of a causal effect of a treatment action at a single point in time on the mean of an outcome within a population defined by strata of some of the observed covariates. Marginal structural models (MSM) are models for marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates, and are therefore precisely modelling such causal effects. These models were introduced by Robins (e.g., Robins (2000a), Robins (2000b), van der Laan and Robins (2002)). Inverse probability of treatment weighted estimators, double robust inverse probability of treatment weighted estimators, and G-computation (likelihood)-based estimators, have been developed and studied in detail (Robins (2000b), Neugebauer and van der Laan (2004), Yu and van der Laan (2003), van der Laan and Robins (2002)). These methods require from the user specification of a parametric model (i.e., a marginal structural model) for the treatment specific mean as function of the treatment and adjustment covariates, and a model for the nuisance parameter representing either the treatment mechanism (IPTW) and/or regression of the outcome on treatment and all baseline covariates (DR-IPTW, G-comp). In this article we develop and implement a general data adaptive loss-based estimation (as in machine learning) methodology, involving cross-validation to data adaptively select model complexities for the marginal structural model, as well as the nuisance parameter model. We implemented our data adaptive methodology in a publicly available R-package, and illustrate its practical performance with an extensive simulation study. In addition, we provide an application involving the estimation of the effect of lung function on survival in an elderly population.

Suggested Citation

  • Yue Wang & Mark van der Laan, 2004. "Data Adaptive Estimation of the Treatment Specific Mean," U.C. Berkeley Division of Biostatistics Working Paper Series 1159, Berkeley Electronic Press.
    • Handle: RePEc:bep:ucbbio:1159
    Note: oai:bepress.com:ucbbiostat-1159
    as

    Download full text from publisher

    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1159&context=ucbbiostat
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark van der Laan & Maya Petersen, 2004. "Estimation of Direct and Indirect Causal Effects in Longitudinal Studies," U.C. Berkeley Division of Biostatistics Working Paper Series 1155, Berkeley Electronic Press.
    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. Haight, Thaddeus J. & Wang, Yue & van der Laan, Mark J. & Tager, Ira B., 2010. "A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3080-3094, December.

    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. Samuel D. Lendle & Meenakshi S. Subbaraman & Mark J. van der Laan, 2013. "Identification and Efficient Estimation of the Natural Direct Effect among the Untreated," Biometrics, The International Biometric Society, vol. 69(2), pages 310-317, June.
    2. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    3. Zheng Wenjing & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation of Natural Direct Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-40, January.
    4. Susan Athey & Raj Chetty & Guido Imbens & Hyunseung Kang, 2016. "Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index," Papers 1603.09326, arXiv.org, revised Feb 2020.
    5. van der Laan Mark J. & Petersen Maya L, 2008. "Direct Effect Models," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-27, October.
    6. Fardad Zand & Cees van Beers, 2010. "Enterprise Systems Adoption and Firm Performance in Europe: The Role of Innovation," DRUID Working Papers 10-26, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.

    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:bep:ucbbio:1159. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.bepress.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.