IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v79y2017i5p1509-1525.html
   My bibliography  Save this article

Robust estimation of encouragement design intervention effects transported across sites

Author

Listed:
  • Kara E. Rudolph
  • Mark J. Laan

Abstract

No abstract is available for this item.

Suggested Citation

  • Kara E. Rudolph & Mark J. Laan, 2017. "Robust estimation of encouragement design intervention effects transported across sites," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1509-1525, November.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:5:p:1509-1525
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssb.12213
    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. 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.
    2. 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.
    3. Jeffrey R Kling & Jeffrey B Liebman & Lawrence F Katz, 2007. "Experimental Analysis of Neighborhood Effects," Econometrica, Econometric Society, vol. 75(1), pages 83-119, January.
    4. Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
    5. 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.
    6. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
    7. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    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. Rui Chen & Guanhua Chen & Menggang Yu, 2023. "Entropy balancing for causal generalization with target sample summary information," Biometrics, The International Biometric Society, vol. 79(4), pages 3179-3190, December.
    2. Issa J. Dahabreh & Sarah E. Robertson & Lucia C. Petito & Miguel A. Hernán & Jon A. Steingrimsson, 2023. "Efficient and robust methods for causally interpretable meta‐analysis: Transporting inferences from multiple randomized trials to a target population," Biometrics, The International Biometric Society, vol. 79(2), pages 1057-1072, June.
    3. Fan Li & Ashley L. Buchanan & Stephen R. Cole, 2022. "Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 669-697, June.
    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. Xinyu Li & Wang Miao & Fang Lu & Xiao‐Hua Zhou, 2023. "Improving efficiency of inference in clinical trials with external control data," Biometrics, The International Biometric Society, vol. 79(1), pages 394-403, March.
    6. Dasom Lee & Shu Yang & Lin Dong & Xiaofei Wang & Donglin Zeng & Jianwen Cai, 2023. "Improving trial generalizability using observational studies," Biometrics, The International Biometric Society, vol. 79(2), pages 1213-1225, June.
    7. Sujin Park & Ali Tafti & Galit Shmueli, 2024. "Transporting Causal Effects Across Populations Using Structural Causal Modeling: An Illustration to Work-from-Home Productivity," Information Systems Research, INFORMS, vol. 35(2), pages 686-705, June.
    8. Guanbo Wang & Mireille E. Schnitzer & Dick Menzies & Piret Viiklepp & Timothy H. Holtz & Andrea Benedetti, 2020. "Estimating treatment importance in multidrug‐resistant tuberculosis using Targeted Learning: An observational individual patient data network meta‐analysis," Biometrics, The International Biometric Society, vol. 76(3), pages 1007-1016, September.
    9. Melody Y Huang & Harsh Parikh, 2024. "Towards Generalizing Inferences from Trials to Target Populations," Papers 2402.17042, arXiv.org, revised May 2024.
    10. Benjamin Lu & Eli Ben-Michael & Avi Feller & Luke Miratrix, 2023. "Is It Who You Are or Where You Are? Accounting for Compositional Differences in Cross-Site Treatment Effect Variation," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 420-453, August.
    11. Lauren Cappiello & Zhiwei Zhang & Changyu Shen & Neel M. Butala & Xinping Cui & Robert W. Yeh, 2021. "Adjusting for population differences using machine learning methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 750-769, 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. 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.
    2. Gruber Susan & van der Laan Mark J., 2010. "An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-31, May.
    3. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    4. 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.
    5. 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.
    6. van der Laan Mark J. & Petersen Maya & Zheng Wenjing, 2013. "Estimating the Effect of a Community-Based Intervention with Two Communities," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 83-106, June.
    7. 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.
    8. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    9. Stitelman Ori M & van der Laan Mark J., 2010. "Collaborative Targeted Maximum Likelihood for Time to Event Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-46, June.
    10. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    11. Lisa Sanbonmatsu & Jeffrey R. Kling & Greg J. Duncan & Jeanne Brooks-Gunn, 2006. "Neighborhoods and Academic Achievement: Results from the Moving to Opportunity Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 41(4).
    12. 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.
    13. 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.
    14. 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.
    15. Wei Luo & Yeying Zhu & Debashis Ghosh, 2017. "On estimating regression-based causal effects using sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 104(1), pages 51-65.
    16. Jens Ludwig & Jeffrey R. Kling & Sendhil Mullainathan, 2011. "Mechanism Experiments and Policy Evaluations," Journal of Economic Perspectives, American Economic Association, vol. 25(3), pages 17-38, Summer.
    17. Bezawit Adugna Bahru & Manfred Zeller, 2022. "Gauging the impact of Ethiopia’s productive safety net programme on agriculture: Application of targeted maximum likelihood estimation approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 257-276, February.
    18. Brooks Jordan & van der Laan Mark J. & Go Alan S., 2012. "Targeted Maximum Likelihood Estimation for Prediction Calibration," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-35, October.
    19. repec:mpr:mprres:8128 is not listed on IDEAS
    20. Pesola, Hanna Onerva & Sarvimäki, Matti, 2022. "Intergenerational Spillovers of Integration Policies: Evidence from Finland's Integration Plans," IZA Discussion Papers 15310, Institute of Labor Economics (IZA).
    21. 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.

    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:jorssb:v:79:y:2017:i:5:p:1509-1525. 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: https://edirc.repec.org/data/rssssea.html .

    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.