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Bias reduction using surrogate endpoints as auxiliary variables

Author

Listed:
  • Yoshiharu Takagi

    (Sanofi K.K.)

  • Yutaka Kano

    (Osaka University)

Abstract

Recently, it is becoming more active to apply appropriate statistical methods dealing with missing data in clinical trials. Under not missing at random missingness, MLE based on direct-likelihood, or observed likelihood, possibly has a serious bias. A solution to the bias problem is to add auxiliary variables such as surrogate endpoints to the model for the purpose of reducing the bias. We theoretically studied the impact of an auxiliary variable on MLE and evaluated the bias reduction or inflation in the case of several typical correlation structures.

Suggested Citation

  • Yoshiharu Takagi & Yutaka Kano, 2019. "Bias reduction using surrogate endpoints as auxiliary variables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 837-852, August.
  • Handle: RePEc:spr:aistmt:v:71:y:2019:i:4:d:10.1007_s10463-018-0667-8
    DOI: 10.1007/s10463-018-0667-8
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    References listed on IDEAS

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    1. Yun Li & Jeremy M. G. Taylor & Roderick J. A. Little, 2011. "A Shrinkage Approach for Estimating a Treatment Effect Using Intermediate Biomarker Data in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(4), pages 1434-1441, December.
    2. Joseph G. Ibrahim & Stuart R. Lipsitz & Nick Horton, 2001. "Using auxiliary data for parameter estimation with non‐ignorably missing outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 361-373.
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