Kim, J.S. (2003) Maximum Likelihood Estimation for the Proportional Hazards Model with Partly Interval-Censored Data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65, 489-502.
https://doi.org/10.1111/1467-9868.00398

has been cited by the following article:

• TITLE: Maximum Likelihood Estimation for the Pooled Repeated Partly Interval-Censored Observations Logistic Regression Model

  AUTHORS: Naghmeh Daneshi, Jong Sung Kim

  KEYWORDS: EM Algorithm, Longitudinal Studies, Louis’ Method, Partly Interval-Censored Failure Time Data, Pooled Repeated Observations

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.1, February 26, 2021

  ABSTRACT: Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of interest has already happened when they come back. In this case, not only are their event times interval-censored, but also their time-dependent measurements are incomplete. This problem was motivated by a national longitudinal survey of youth data. Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation. Then missing information principle is applied to estimate the variance-covariance matrix of the MLEs. Simulation studies demonstrate that the proposed method works well in terms of bias, standard error, and power for samples of moderate size. The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration.