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.
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Acknowledgements
This work was supported by the Natural Science and Engineering Research Council of Canada through grants RGPIN 115928 (Dr. Leilei Zeng) and RGPIN 03688-2016 (Dr. Mary E. Thompson).
Funding
This work is part of Yidan Shi’s PhD thesis co-supervised by Dr. Leilei Zeng and Dr. Mary E. Thompson. Dr. Suzanne L. Tyas contributed to the idea of utilizing auxiliary survival information from the Nun Study - Mortality Study to enhance the efficiency of the estimation. She also provided valuable feedback on the manuscript and gave the permission to use the data from the Nun Study - Aging study.
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Shi, Y., Zeng, L., Thompson, M.E. et al. Augmented likelihood for incorporating auxiliary information into left-truncated data. Lifetime Data Anal 27, 460–480 (2021). https://doi.org/10.1007/s10985-021-09524-6
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DOI: https://doi.org/10.1007/s10985-021-09524-6