A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup

2021 ◽  
pp. 096228022110239
Author(s):  
Liuquan Sun ◽  
Shuwei Li ◽  
Lianming Wang ◽  
Xinyuan Song
Keyword(s):  
Censored Data ◽  
Failure Time ◽  
Interval Censoring ◽  
Childhood Mortality ◽  
Interval Censored Data ◽  
Finite Sample ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Interval Censored ◽  
Model Approach

Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.

scholarly journals Variable selection in a flexible parametric mixture cure model with interval‐censored data

2015 ◽  
Vol 35 (7) ◽  
pp. 1210-1225 ◽  
Author(s):  
Sylvie Scolas ◽  
Anouar El Ghouch ◽  
Catherine Legrand ◽  
Abderrahim Oulhaj
Keyword(s):  
Variable Selection ◽  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Interval Censored

scholarly journals A semiparametric cure model for interval-censored data

2013 ◽  
Vol 55 (5) ◽  
pp. 771-788 ◽  
Author(s):  
Kwok Fai Lam ◽  
Kin Yau Wong ◽  
Feifei Zhou
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Interval Censored

Tutorial on methods for interval-censored data and their implementation in R

2009 ◽  
Vol 9 (4) ◽  
pp. 259-297 ◽  
Author(s):  
Guadalupe Gómez ◽  
M Luz Calle ◽  
Ramon Oller ◽  
Klaus Langohr
Keyword(s):  
Censored Data ◽  
Time Window ◽  
Interval Censoring ◽  
Right Censoring ◽  
Interval Censored Data ◽  
Survival Curves ◽  
Statistical Software ◽  
The Past ◽  
Interval Censored ◽  
Non Parametric

Interval censoring is encountered in many practical situations when the event of interest cannot be observed and it is only known to have occurred within a time window. The theory for the analysis of interval-censored data has been developed over the past three decades and several reviews have been written. However, it is still a common practice in medical and reliability studies to simplify the interval censoring structure of the data into a more standard right censoring situation by, for instance, imputing the midpoint of the censoring interval. The availability of software for right censoring might well be the main reason for this simplifying practice. In contrast, several methods have been developed to deal with interval-censored data and the corresponding algorithms to make the procedures feasible are scattered across the statistical software or remain behind the personal computers of many researchers. The purpose of this tutorial is to present, in a pedagogical and unified manner, the methodology and the available software for analyzing interval-censored data. The paper covers frequentist non-parametric, parametric and semiparametric estimating approaches, non-parametric tests for comparing survival curves and a section on simulation of interval-censored data. The methods and the software are described using the data from a dental study.

scholarly journals Regression Models for Interval Censored Data Using Parametric Pseudo-Observations

2020 ◽  
Author(s):  
Martin Nygård Johansen ◽  
Søren Lundbye-Christensen ◽  
Jacob Moesgaard Larsen ◽  
Erik Thorlund Parner
Keyword(s):  
Censored Data ◽  
Interval Censoring ◽  
Regression Modeling ◽  
Implantable Cardioverter Defibrillators ◽  
Interval Censored Data ◽  
Right Censored Data ◽  
Association Measures ◽  
Real Dataset ◽  
Interval Censored ◽  
Non Parametric

Abstract Background: Time-to-event data that is subject to interval censoring is common in the practice of medical research and versatile statistical methods for estimating associations in such settings have been limited. For right censored data, non-parametric pseudo-observations have been proposed as a basis for regression modeling with the possibility to use different association measures. In this article, we propose a method for calculating pseudo-observations for interval censored data. Methods: We develop an extension of a recently developed set of parametric pseudo-observations based on a spline-based flexible parametric estimator. The inherent competing risk issue with an interval censored event of interest necessitates the use of an illness-death model, and we formulate our method within this framework. To evaluate the empirical properties of the proposed method, we perform a simulation study and calculate pseudo-observations based on our method as well as alternative approaches. We also present an analysis of a real dataset on patients with implantable cardioverter-defibrillators who are monitored for the occurrence of a particular type of device failures by routine follow-up examinations. In this dataset, we have information on exact event times as well as the interval censored data, so we can compare analyses of pseudo-observations based on the interval censored data to those obtained using the non-parametric pseudo-observations for right censored data. Results: Our simulations show that the proposed method for calculating pseudo-observations provides unbiased estimates of the cumulative incidence function as well as associations with exposure variables with appropriate coverage probabilities. The analysis of the real dataset also suggests that our method provides estimates which are in agreement with estimates obtained from the right censored data. Conclusions: The proposed method for calculating pseudo-observations based on the flexible parametric approach provides a versatile solution to the specific challenges that arise with interval censored data. This solution allows regression modeling using a range of different association measures.

Statistical analysis of bivariate interval-censored failure time data

2015 ◽  
Author(s):  
◽  
Qingning Zhou
Keyword(s):  
Statistical Analysis ◽  
Censored Data ◽  
Failure Time ◽  
Current Status Data ◽  
Current Status ◽  
Interval Censored Data ◽  
Time Data ◽  
Failure Times ◽  
Interval Censored ◽  
Status Data

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Interval-censored failure time data arise when the failure time of interest in a survival study is not exactly observed but known only to fall within some interval. One area that often produces such data is medical studies with periodic follow-ups, in which the medical condition of interest such as the onset of a disease is only known to occur between two adjacent examination times. An important special case of intervalcensored data is current status data which arise when each study subject is observed only once and the only information available is whether the failure event of interest has occurred or not by the observation time. The areas that often yield such data include tumorigenicity experiments and cross-sectional studies. Sometimes we refer to current status data as case I interval-censored data, and the general case as case II interval-censored data. The analysis of both case I and case II interval-censored data has recently attracted a great deal of attention and many procedures have been proposed for various issues related to it. However, there are still a number of problems that remain unsolved or lack approaches that are simpler, more efficient and apply to more general situations compared to the existing ones. This is especially the case for multivariate intervalcensored data which arise if there are multiple failure times of interest and all of them suffer intervalcensoring. This dissertation focuses on the statistical analysis for bivariate interval-censored data, including regression analysis, model selection and estimation of the association between failure times.

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