IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v68y2012i1p233-235.html
   My bibliography  Save this article

Discussion Contribution to 091037PR4 (Ghosh, Taylor, and Sargent)

Author

Listed:
  • Geert Molenberghs

Abstract

No abstract is available for this item.

Suggested Citation

  • Geert Molenberghs, 2012. "Discussion Contribution to 091037PR4 (Ghosh, Taylor, and Sargent)," Biometrics, The International Biometric Society, vol. 68(1), pages 233-235, March.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:1:p:233-235
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01634.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. Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
    2. Marshall M. Joffe & Tom Greene, 2009. "Related Causal Frameworks for Surrogate Outcomes," Biometrics, The International Biometric Society, vol. 65(2), pages 530-538, June.
    3. Ariel Alonso & Helena Geys & Geert Molenberghs & Michael G. Kenward & Tony Vangeneugden, 2004. "Validation of Surrogate Markers in Multiple Randomized Clinical Trials with Repeated Measurements: Canonical Correlation Approach," Biometrics, The International Biometric Society, vol. 60(4), pages 845-853, December.
    4. Ariel Alonso & Geert Molenberghs, 2007. "Surrogate Marker Evaluation from an Information Theory Perspective," Biometrics, The International Biometric Society, vol. 63(1), pages 180-186, March.
    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. Renfro, Lindsay A. & Shi, Qian & Xue, Yuan & Li, Junlong & Shang, Hongwei & Sargent, Daniel J., 2014. "Center-within-trial versus trial-level evaluation of surrogate endpoints," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 1-20.
    2. Acharya, Avidit & Blackwell, Matthew & Sen, Maya, 2016. "Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects," American Political Science Review, Cambridge University Press, vol. 110(3), pages 512-529, August.
    3. Ariel Alonso & Geert Molenberghs, 2007. "Surrogate Marker Evaluation from an Information Theory Perspective," Biometrics, The International Biometric Society, vol. 63(1), pages 180-186, March.
    4. Brenden Bishop & Minjeong Jeon, 2016. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1164-1167, December.
    5. Ghosh, Debashis, 2012. "A causal framework for surrogate endpoints with semi-competing risks data," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1898-1902.
    6. Mohamed Boly, 2018. "CO 2 mitigation in developing countries: the role of foreign aid," Working Papers halshs-01740881, HAL.
    7. Morten Overgaard & Stefan Nygaard Hansen, 2021. "On the assumption of independent right censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1234-1255, December.
    8. Shu Xu & Shelley A. Blozis, 2011. "Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 237-256, April.
    9. Casimir Ledoux Sofeu & Virginie Rondeau, 2020. "How to use frailtypack for validating failure-time surrogate endpoints using individual patient data from meta-analyses of randomized controlled trials," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-25, January.
    10. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    11. Yun Li & Jeremy M.G. Taylor & Michael R. Elliott, 2010. "A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 523-531, June.
    12. Kott Phillip S. & Liao Dan, 2018. "Calibration Weighting for Nonresponse with Proxy Frame Variables (So that Unit Nonresponse Can Be Not Missing at Random)," Journal of Official Statistics, Sciendo, vol. 34(1), pages 107-120, March.
    13. Margaux Delporte & Steffen Fieuws & Geert Molenberghs & Geert Verbeke & Simeon Situma Wanyama & Elpis Hatziagorou & Christiane De Boeck, 2022. "A joint normal‐binary (probit) model," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 37-51, December.
    14. Caroline Beunckens & Cristina Sotto & Geert Molenberghs & Geert Verbeke, 2009. "A multifaceted sensitivity analysis of the Slovenian public opinion survey data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 171-196, May.
    15. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2018. "Missing Data: A Unified Taxonomy Guided by Conditional Independence," International Statistical Review, International Statistical Institute, vol. 86(2), pages 189-204, August.
    16. Hairu Wang & Zhiping Lu & Yukun Liu, 2023. "Score test for missing at random or not under logistic missingness models," Biometrics, The International Biometric Society, vol. 79(2), pages 1268-1279, June.
    17. Andrew T. Karl & Yan Yang & Sharon L. Lohr, 2013. "A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 577-603, December.
    18. Layla Parast & Tianxi Cai & Lu Tian, 2023. "Testing for heterogeneity in the utility of a surrogate marker," Biometrics, The International Biometric Society, vol. 79(2), pages 799-810, June.
    19. Rianne Margaretha Schouten & Gerko Vink, 2021. "The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions," Sociological Methods & Research, , vol. 50(3), pages 1243-1258, August.
    20. Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.

    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:68:y:2012:i:1:p:233-235. 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.