Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: Application to disability in elderly

Abstract

We aim to evaluate the marginal effects of covariates on time-to-disability in the elderly under the semi-competing risks framework, as death dependently censors disability, not vice versa. It becomes particularly challenging when time-to-disability is subject to interval censoring due to intermittent assessments. A left truncation issue arises when the age time scale is applied. We develop a flexible two-parameter copula-based semiparametric transformation model for semi-competing risks data subject to interval censoring and left truncation. The two-parameter copula quantifies both upper and lower tail dependence between two margins. The semiparametric transformation models incorporate proportional hazards and proportional odds models in both margins. We propose a two-step sieve maximum likelihood estimation procedure and study the sieve estimators’ asymptotic properties. Simulations show that the proposed method corrects biases in the marginal method. We demonstrate the proposed method in a large-scale Chinese Longitudinal Healthy Longevity Study and provide new insights into preventing disability in the elderly. The proposed method could be applied to the general semi-competing risks data with intermittently assessed disease status.

Regression Analysis of Dependent Current Status Data with Left Truncation

“…Under left truncation, individuals with smaller event times are less likely to be observed, leading to bias in the research sample toward larger event times. Currently, some literature studies have developed regression analyses of current status data with left truncation [16,17]. In the following, we will discuss the regression analyses of current status data with left truncation and informative censoring.…”