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Augmented likelihood for incorporating auxiliary information into left-truncated data

Author

Listed:
  • Yidan Shi

    (University of Waterloo)

  • Leilei Zeng

    (University of Waterloo)

  • Mary E. Thompson

    (University of Waterloo)

  • Suzanne L. Tyas

    (University of Waterloo)

Abstract

Time-to-event data are often subject to left-truncation. Lack of consideration of the sampling condition will introduce bias and loss in efficiency of the estimation. While auxiliary information from the same or similar cohorts may be available, challenges arise due to the practical issue of accessibility of individual-level data and taking account of various sampling conditions for different cohorts. In this paper, we introduce a likelihood-based method to incorporate information from auxiliary data to eliminate the left-truncation problem and improve efficiency. A one-step Monte-Carlo Expectation-Maximization algorithm is developed to calculate an augmented likelihood through creating pseudo-data sets which extend the form and conditions of the observed sample. The method is illustrated by both a real dataset and simulation studies.

Suggested Citation

  • Yidan Shi & Leilei Zeng & Mary E. Thompson & Suzanne L. Tyas, 2021. "Augmented likelihood for incorporating auxiliary information into left-truncated data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 460-480, July.
    • Handle: RePEc:spr:lifeda:v:27:y:2021:i:3:d:10.1007_s10985-021-09524-6
    DOI: 10.1007/s10985-021-09524-6
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    References listed on IDEAS

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    1. Cheryl L. Faucett & Nathaniel Schenker & Jeremy M. G. Taylor, 2002. "Survival Analysis Using Auxiliary Variables Via Multiple Imputation, with Application to AIDS Clinical Trial Data," Biometrics, The International Biometric Society, vol. 58(1), pages 37-47, March.
    2. Jing Qin & Yu Shen, 2010. "Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model," Biometrics, The International Biometric Society, vol. 66(2), pages 382-392, June.
    3. Gang Li & Jing Qin, 1998. "Semiparametric likelihood‐based inference for biased and truncated data when the total sample size is known," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 243-254.
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