Augmented likelihood for incorporating auxiliary information into left-truncated data
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 accessi...
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| Veröffentlicht in: | Lifetime data analysis 2021-07, Vol.27 (3), p.460-480 |
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| container_title | Lifetime data analysis |
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| creator | Shi, Yidan Zeng, Leilei Thompson, Mary E. Tyas, Suzanne L. |
| description | 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. |
| doi_str_mv | 10.1007/s10985-021-09524-6 |
| format | Article |
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| source | Business Source Complete; Springer Nature - Complete Springer Journals |
| subjects | Aging Algorithms Calendars Disease Economics Efficiency Finance Health Sciences Insurance Japanese Americans Management Mathematics and Statistics Medicine Monte Carlo simulation Mortality Nuns Operations Research/Decision Theory Quality Control Reliability Religion Safety and Risk Sampling Statistical methods Statistics Statistics for Business Statistics for Life Sciences Survival analysis |
| title | Augmented likelihood for incorporating auxiliary information into left-truncated data |
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