Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction

• Published in: Lifetime data analysis Vol. 28; no. 1; pp. 68 - 88
• Main Authors: Shen, Pao-sheng, Peng, Yingwei, Chen, Hsin-Jen, Chen, Chyong-Mei
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2022; Springer Nature B.V
• Subjects: Age; Algorithms; Bias; Cohort Studies; Cross-Sectional Studies; Design; Diabetes; Diabetes mellitus; Disease; Economics; Epidemiology; Estimates; Failure times; Finance; Graphical methods; Health Sciences; Humans; Insurance; Likelihood Functions; Management; Mathematics and Statistics; Maximum likelihood estimates; Maximum likelihood estimation; Medicine; Nonparametric statistics; Operations Research/Decision Theory; Proportional Hazards Models; Public health; Quality Control; Regression models; Reliability; Safety and Risk; Sampling; Statistical analysis; Statistical models; Statistics; Statistics for Business; Statistics for Life Sciences; Survival; Survival Analysis; Left truncation; Interval censoring; Nonparametric maximum likelihood estimation; Mixture cure model; Length-biased sampling
• ISSN: 1380-7870; 1572-9249; 1572-9249
• DOI: 10.1007/s10985-021-09536-2

Left-truncated data are often encountered in epidemiological cohort studies, where individuals are recruited according to a certain cross-sectional sampling criterion. Length-biased data, a special case of left-truncated data, assume that the incidence of the initial event follows a homogeneous Poisson process. In this article, we consider an analysis of length-biased and interval-censored data with a nonsusceptible fraction. We first point out the importance of a well-defined target population, which depends on the prior knowledge for the support of the failure times of susceptible individuals. Given the target population, we proceed with a length-biased sampling and draw valid inferences from a length-biased sample. When there is no covariate, we show that it suffices to consider a discrete version of the survival function for the susceptible individuals with jump points at the left endpoints of the censoring intervals when maximizing the full likelihood function, and propose an EM algorithm to obtain the nonparametric maximum likelihood estimates of nonsusceptible rate and the survival function of the susceptible individuals. We also develop a novel graphical method for assessing the stationarity assumption. When covariates are present, we consider the Cox proportional hazards model for the survival time of the susceptible individuals and the logistic regression model for the probability of being susceptible. We construct the full likelihood function and obtain the nonparametric maximum likelihood estimates of the regression parameters by employing the EM algorithm. The large sample properties of the estimates are established. The performance of the method is assessed by simulations. The proposed model and method are applied to data from an early-onset diabetes mellitus study.