Work

Missing Information Principle: A Unified Approach for General Truncated and Censored Survival Data Problems

Year: 2018

Type: article

Abstract: It is well known that truncated survival data are subject to sampling bias, where the sampling weight depends on the underlying truncation time distribution. Recently, there has been a rising interest in developing methods to better exploit the information about the truncation time, thus the sampling weight function, to obtain more efficient estimation. In this paper, we propose to treat truncation and censoring as "missing data mechanism" and apply the missing information principle to develop a unified framework for analyzing left-truncated and right-censored data with unspecified or known truncation time distributions. Our framework is structured in a way that is easy to understand and enjoys a great flexibility for handling different types of models. Moreover, a new test for checking the independence between the underlying truncation time and survival time is derived along the same line. The proposed hypothesis testing procedure utilizes all observed data and hence can yield a much higher power than the conditional Kendall's tau test that only involves comparable pairs of observations under truncation. Simulation studies with practical sample sizes are conducted to compare the performance of the proposed method with its competitors. The proposed methodologies are applied to a dementia study and a nursing house study for illustration.

Source: Statistical Science

DOI: 10.1214/17-sts638

Authors: Yifei Sun, Jing Qin, Chiung-Yu Huang

Institutions: Columbia University, National Institutes of Health, National Institute of Allergy and Infectious Diseases, University of California, San Francisco

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Cancer Institute

Award: G5241214390