Smoothing methods to estimate the hazard rate under double truncation
Carla Moreira, Jacobo de U\~na-\'Alvarez, Ana Cristina Santos, and Henrique Barros

TL;DR
This paper introduces a flexible kernel smoothing method for estimating hazard rates in survival analysis with double truncation, addressing bias correction and providing theoretical and simulation validation.
Contribution
It proposes a novel kernel-based hazard rate estimator under double truncation, combining nonparametric and semiparametric approaches with theoretical properties and practical bandwidth selection.
Findings
Semiparametric approach improves accuracy over fully nonparametric methods.
Derived asymptotic mean integrated squared error for bandwidth selection.
Validated methods through simulations and real data applications.
Abstract
In Survival Analysis, the observed lifetimes often correspond to individuals for which the event occurs within a specific calendar time interval. With such interval sampling, the lifetimes are doubly truncated at values determined by the birth dates and the sampling interval. This double truncation may induce a systematic bias in estimation, so specific corrections are needed. A relevant target in Survival Analysis is the hazard rate function, which represents the instantaneous probability for the event of interest. In this work we introduce a flexible estimation approach for the hazard rate under double truncation. Specifically, a kernel smoother is considered, in both a fully nonparametric setting and a semiparametric setting in which the incidence process fits a given parametric model. Properties of the kernel smoothers are investigated both theoretically and through simulations. In…
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Taxonomy
TopicsStatistical Methods and Inference · Liver Disease Diagnosis and Treatment · Genetic and phenotypic traits in livestock
