IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i6p1457-1466.html
   My bibliography  Save this article

Simplified modeling strategies for surrogate validation with multivariate failure-time data

Author

Listed:
  • Cortiñas Abrahantes, José
  • Burzykowski, Tomasz

Abstract

The linear mixed effects model has become a standard tool for the analysis of continuous hierarchical data such as, for example, repeated measures or data from meta-analyses. However, in certain situations the model does pose unavoidable computational problems. In the context of surrogate markers, this problem has appeared when using an estimation and prediction-based approach for the evaluation of surrogate endpoints. Convergence problems can occur mainly due to small between-trial variability or small number of trials. A number of alternative strategies has been proposed and studied for normally distributed data, but not such study has been conducted for other types of endpoints. The idea is to study if such simplified strategies, which always ignore individual level surrogacy, can also be applied when both surrogate and true endpoints are of failure-time types. It is shown via simulations that the 3 simplified strategies produced biased estimates, especially for the cases in which the strength of individual level association is different from the strength of trial level association. For this reason, it is recommended not to use simplified strategies when dealing with failure-time data, in contrast to the case of normally distributed data, for which simplified strategies are recommended. Possible reasons for this discrepancy might be that, in this case, ignoring the individual level association influences estimates of the mean structure parameters, what results in distorted estimates of the trial level association.

Suggested Citation

  • Cortiñas Abrahantes, José & Burzykowski, Tomasz, 2010. "Simplified modeling strategies for surrogate validation with multivariate failure-time data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1457-1466, June.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1457-1466
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00031-9
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Abrahantes, Jose Cortinas & Molenberghs, Geert & Burzykowski, Tomasz & Shkedy, Ziv & Abad, Ariel Alonso & Renard, Didier, 2004. "Choice of units of analysis and modeling strategies in multilevel hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 537-563, October.
    2. Ariel Alonso & Geert Molenberghs & Tomasz Burzykowski & Didier Renard & Helena Geys & Ziv Shkedy & Fabián Tibaldi & José Cortiñas Abrahantes & Marc Buyse, 2004. "Prentice's Approach and the Meta-Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 60(3), pages 724-728, September.
    3. Abrahantes, Jose Cortinas & Legrand, Catherine & Burzykowski, Tomasz & Janssen, Paul & Ducrocq, Vincent & Duchateau, Luc, 2007. "Comparison of different estimation procedures for proportional hazards model with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3913-3930, May.
    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. 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.
    3. Munda, Marco & Rotolo, Federico & Legrand, Catherine, 2012. "parfm: Parametric Frailty Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i11).
    4. Rotolo, Federico & Legrand, Catherine & Van Keilegom, Ingrid, 2011. "Simulation of clustered multi-state survival data based on a copula model," LIDAM Discussion Papers ISBA 2011040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. John O'Quigley & Philippe Flandre, 2006. "Quantification of the Prentice Criteria for Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 62(1), pages 297-300, March.
    6. Yongming Qu & Michael Case, 2007. "Quantifying the Effect of the Surrogate Marker by Information Gain," Biometrics, The International Biometric Society, vol. 63(3), pages 958-960, September.
    7. Rui Zhuang & Ying Qing Chen, 2020. "Measuring Surrogacy in Clinical Research," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 295-323, December.
    8. Lee, Kyeong Eun & Kim, Yongku & Xu, Ronghui, 2014. "Bayesian variable selection under the proportional hazards mixed-effects model," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 53-65.
    9. Ariel Alonso & Wim Van der Elst & Geert Molenberghs & Marc Buyse & Tomasz Burzykowski, 2015. "On the relationship between the causal-inference and meta-analytic paradigms for the validation of surrogate endpoints," Biometrics, The International Biometric Society, vol. 71(1), pages 15-24, March.
    10. Tilahun, Abel & Pryseley, Assam & Alonso, Ariel & Molenberghs, Geert, 2007. "Flexible surrogate marker evaluation from several randomized clinical trials with continuous endpoints, using R and SAS," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4152-4163, May.
    11. P. Economou & S. Malefaki & C. Caroni, 2015. "Bayesian Threshold Regression Model with Random Effects for Recurrent Events," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 871-898, December.
    12. Pryseley, Assam & Tchonlafi, Clotaire & Verbeke, Geert & Molenberghs, Geert, 2011. "Estimating negative variance components from Gaussian and non-Gaussian data: A mixed models approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1071-1085, February.
    13. Munda, Marco & Rotolo, Federico & Legrand, Catherine, 2012. "parfm: Parametric Frailty Models in R," LIDAM Discussion Papers ISBA 2012005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2009. "Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 637-654, June.

    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:eee:csdana:v:54:y:2010:i:6:p:1457-1466. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.