IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v59y2003i4p1046-1055.html
   My bibliography  Save this article

Semiparametric Estimation of Treatment Effect in a Pretest-Posttest Study

Author

Listed:
  • Selene Leon
  • Anastasios A. Tsiatis
  • Marie Davidian

Abstract

No abstract is available for this item.

Suggested Citation

  • Selene Leon & Anastasios A. Tsiatis & Marie Davidian, 2003. "Semiparametric Estimation of Treatment Effect in a Pretest-Posttest Study," Biometrics, The International Biometric Society, vol. 59(4), pages 1046-1055, December.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:4:p:1046-1055
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2003.00120.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. Yang L. & Tsiatis A. A., 2001. "Efficiency Study of Estimators for a Treatment Effect in a Pretest-Posttest Trial," The American Statistician, American Statistical Association, vol. 55, pages 314-321, 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. Rosenblum Michael & van der Laan Mark J., 2010. "Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-44, April.
    2. Yoguel, Gabriel & Pereira, Mariano & Barletta, Florencia, 2016. "The performance of the computer software and services sector in Argentina: microeconometric evidence on public-sector support programmes," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.
    3. Bédhat Jean-Marc Atsebi & Jean-Louis Combes & Alexandru Minea, 2019. "The trade costs of financial crisis," Post-Print hal-02315234, HAL.
    4. Brian J. Reich & Howard D. Bondell & Lexin Li, 2011. "Sufficient Dimension Reduction via Bayesian Mixture Modeling," Biometrics, The International Biometric Society, vol. 67(3), pages 886-895, September.

    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. Jitendra Ganju, 2004. "Some Unexamined Aspects of Analysis of Covariance in Pretest–Posttest Studies," Biometrics, The International Biometric Society, vol. 60(3), pages 829-833, September.
    2. J. R. Lockwood & Daniel F. McCaffrey, 2019. "Impact Evaluation Using Analysis of Covariance With Error-Prone Covariates That Violate Surrogacy," Evaluation Review, , vol. 43(6), pages 335-369, December.
    3. Peter Z. Schochet, 2013. "Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 219-238, June.
    4. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
    5. repec:mpr:mprres:6372 is not listed on IDEAS
    6. Pierre Chausse & George Luta, 2017. "Casual Inference using Generalized Empirical Likelihood Methods," Working Papers 1707, University of Waterloo, Department of Economics, revised Dec 2017.
    7. Min Zhang & Anastasios A. Tsiatis & Marie Davidian, 2008. "Improving Efficiency of Inferences in Randomized Clinical Trials Using Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 64(3), pages 707-715, September.
    8. John A. List & Azeem M. Shaikh & Atom Vayalinkal, 2023. "Multiple testing with covariate adjustment in experimental economics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 920-939, September.
    9. Peter Z. Schochet, 2018. "Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 568-593, October.
    10. Azzam, Tarek & Bates, Michael D. & Fairris, David, 2022. "Do learning communities increase first year college retention? Evidence from a randomized control trial," Economics of Education Review, Elsevier, vol. 89(C).
    11. repec:mpr:mprres:6094 is not listed on IDEAS
    12. Yujia Gu & Hanzhong Liu & Wei Ma, 2023. "Regression‐based multiple treatment effect estimation under covariate‐adaptive randomization," Biometrics, The International Biometric Society, vol. 79(4), pages 2869-2880, December.
    13. David Benkeser & Iván Díaz & Alex Luedtke & Jodi Segal & Daniel Scharfstein & Michael Rosenblum, 2021. "Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1467-1481, December.
    14. Donald P. Green & Winston Lin & Claudia Gerber, 2018. "Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments," Evaluation Review, , vol. 42(4), pages 391-422, August.
    15. Bai, Yuehao & Jiang, Liang & Romano, Joseph P. & Shaikh, Azeem M. & Zhang, Yichong, 2024. "Covariate adjustment in experiments with matched pairs," Journal of Econometrics, Elsevier, vol. 241(1).
    16. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    17. Jinkook Lee & Drystan Phillips, 2011. "Income and Poverty among Older Koreans Relative Contributions of and Relationship between Public and Family Transfers," Working Papers WR-852, RAND Corporation.
    18. Peter Z. Schochet, 2020. "Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 32-57, February.
    19. Nicholas Williams & Michael Rosenblum & Iván Díaz, 2022. "Optimising precision and power by machine learning in randomised trials with ordinal and time‐to‐event outcomes with an application to COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2156-2178, October.
    20. repec:mpr:mprres:5699 is not listed on IDEAS
    21. Peter Z. Schochet, 2010. "The Late Pretest Problem in Randomized Control Trials of Education Interventions," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 379-406, August.
    22. Peter Z. Schochet, "undated". "The Late Pretest Problem in Randomized Control Trials of Education Interventions," Mathematica Policy Research Reports fb514df5dbb84a5dbea79865c, Mathematica Policy Research.
    23. Jonathan W. Bartlett, 2020. "Robustness of ANCOVA in randomized trials with unequal randomization," Biometrics, The International Biometric Society, vol. 76(3), pages 1036-1038, 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:59:y:2003:i:4:p:1046-1055. 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.