Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917981882155008 |
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| author | Matthews, Spencer Nan, Bin |
| author_facet | Matthews, Spencer Nan, Bin |
| contents | Cohort studies of the onset of a disease often encounter left-truncation on the event time of interest in addition to right-censoring due to variable enrollment times of study participants. Analysis of such event time data can be biased if left-truncation is not handled properly. We propose a semiparametric sieve likelihood approach for fitting a linear regression model to data where the response variable is subject to both left-truncation and right-censoring. We show that the estimators of regression coefficients are consistent, asymptotically normal and semiparametrically efficient. Extensive simulation studies show the effectiveness of the method across a wide variety of error distributions. We further illustrate the method by analyzing a dataset from The 90+ Study for aging and dementia. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_07413 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach Matthews, Spencer Nan, Bin Methodology Cohort studies of the onset of a disease often encounter left-truncation on the event time of interest in addition to right-censoring due to variable enrollment times of study participants. Analysis of such event time data can be biased if left-truncation is not handled properly. We propose a semiparametric sieve likelihood approach for fitting a linear regression model to data where the response variable is subject to both left-truncation and right-censoring. We show that the estimators of regression coefficients are consistent, asymptotically normal and semiparametrically efficient. Extensive simulation studies show the effectiveness of the method across a wide variety of error distributions. We further illustrate the method by analyzing a dataset from The 90+ Study for aging and dementia. |
| title | Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach |
| topic | Methodology |
| url | https://arxiv.org/abs/2504.07413 |