Statistical analysis of disease onset during lifespan with left truncation
Year: 2026
Type: article
Abstract: We consider a new nonparametric method for analyzing the onset of a chronic condition, e.g., dementia, when death occurs frequently in the study population. In contrast to the commonly used semi-competing risks or illness-death model, we study the distribution of the disease onset time conditional on the death time, which consists of two components that provide straightforward interpretations of the disease onset during the lifespan. Without imposing any model assumption, we propose a kernel-weighted product-limit estimator that properly handles the left-truncation, a common issue in aging studies. The estimator is asymptotically normal and its variance can be estimated by a Greenwood-type variance estimator. We use the integrated Brier score for the bandwidth selection and illustrate the proposed method with simulation studies, an analysis of The 90+ Study, and an analysis of the UC Health Data Warehouse.
Source: Biometrics
Authors: Yue Wang, Bin Nan, Daniel L Gillen, Maria M Corrada
Institution: University of California, Irvine
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,780
Awards: New nonparametric theory and methods for censored data, Clinical, Imaging, and Pathological Studies in the Oldest Old: The 90+ Study, Cutting Edge Survival Methods for Epidemiological Data, Statistical Methods for Alzheimer's Research, The Alzheimer's Disease Research Center at the University of California, Irvine
OpenAlex ID: W7167680068