scholarly journals A multiple imputation approach for semiparametric cure model with interval censored data

2016 ◽  
Vol 99 ◽  
pp. 105-114 ◽  
Author(s):  
Jie Zhou ◽  
Jiajia Zhang ◽  
Alexander C. McLain ◽  
Bo Cai
Keyword(s):  
Multiple Imputation ◽  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Interval Censored ◽  
Imputation Approach

Estimating trends in the incidence rate with interval censored data and time-dependent covariates

2019 ◽  
Vol 29 (1) ◽  
pp. 272-281 ◽  
Author(s):  
Alain Vandormael ◽  
Frank Tanser ◽  
Diego Cuadros ◽  
Adrian Dobra
Keyword(s):  
Multiple Imputation ◽  
Incidence Rate ◽  
Censored Data ◽  
Transformation Model ◽  
Time Dependent ◽  
Cumulative Distribution ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Time Dependent Covariates ◽  
Imputation Approach

We propose a multiple imputation method for estimating the incidence rate with interval censored data and time-dependent (and/or time-independent) covariates. The method has two stages. First, we use a semi-parametric G-transformation model to estimate the cumulative baseline hazard function and the effects of the time-dependent (and/or time-independent covariates) on the interval censored infection times. Second, we derive the participant's unique cumulative distribution function and impute infection times conditional on the covariate values. To assess performance, we simulated infection times from a Cox proportional hazards model and induced interval censoring by varying the testing rate, e.g., participants test 100%, 75%, 50% of the time, etc. We then compared the incidence rate estimates from our G-imputation approach with single random-point and mid-point imputation. By comparison, our G-imputation approach gave more accurate incidence rate estimates and appropriate standard errors for models with time-independent covariates only, time-dependent covariates only, and a mixture of time-dependent and time-independent covariates across various testing rates. We demonstrate, for the first time, a multiple imputation approach for incidence rate estimation with interval censored data and time-dependent (and/or time-independent) covariates.

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

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 Multiple imputation for interval censored data with auxiliary variables

2006 ◽  
Vol 26 (4) ◽  
pp. 769-781 ◽  
Author(s):  
Chiu-Hsieh Hsu ◽  
Jeremy M. G. Taylor ◽  
Susan Murray ◽  
Daniel Commenges
Keyword(s):  
Multiple Imputation ◽  
Censored Data ◽  
Interval Censored Data ◽  
Auxiliary Variables ◽  
Interval Censored
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