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

Naghmeh Daneshi,Jong Sung Kim

doi:10.4236/ojs.2021.111012

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.

Highlights

In longitudinal studies, subjects who are likely to progress to a new state during the study are monitored over time
Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation
The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration

Summary

Introduction

Subjects who are likely to progress to a new state during the study are monitored over time. Some subjects complete all of their follow-up visits and their failure times are recorded. Others miss their follow-up visits, and they may learn that the event of interest had already occurred when they came back. There are multiple follow-up visiting intervals for each subject, researchers often use one particular interval that contains the true unknown failure time unless they had accurately determined the failure time. This is known as “partly interval-censored failure time data”. There are quite a few research works based on partly interval-censored data such as [1] [2] [3] and [4] among others

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