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arXiv · 2504.07413

Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach

Spencer Matthews·Bin Nan
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Abstract

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.

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Spencer Matthews, Bin Nan. 2025-04-10. Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach. https://arxiv.org/abs/2504.07413

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