Regression analysis of multivariate interval-censored failure time data with informative censoring

2021 ◽  
pp. 096228022110616
Author(s):  
Mengzhu Yu ◽  
Yanqin Feng ◽  
Ran Duan ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Failure Time ◽  
Asymptotic Properties ◽  
Real Data ◽  
Estimating Equation ◽  
Informative Censoring ◽  
Dependent Censoring ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored

Regression analysis of multivariate interval-censored failure time data has been discussed by many authors1-6. For most of the existing methods, however, one limitation is that they only apply to the situation where the censoring is non-informative or the failure time of interest is independent of the censoring mechanism. It is apparent that this may not be true sometimes and as pointed out by some authors, the analysis that does not take the dependent censoring into account could lead to biased or misleading results7,8. In this study, we consider regression analysis of multivariate interval-censored data arising from the additive hazards model and propose an estimating equation-based approach that allows for the informative censoring. The method can be easily implemented and the asymptotic properties of the proposed estimator of regression parameters are established. Also we perform a simulation study for the evaluation of the proposed method and it suggests that the method works well for practical situations. Finally, the proposed approach is applied to a set of real data.

Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size

2013 ◽  
Vol 9 (2) ◽  
Author(s):  
Xinyan Zhang ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Cluster Size ◽  
Failure Time ◽  
Semiparametric Regression ◽  
Estimating Equation ◽  
Parameter Estimates ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Informative Cluster Size

AbstractClustered interval-censored failure time data are commonly encountered in many medical settings. In such situations, one issue that often arises in practice is that the cluster size is related to the risk for the outcome of interest. It is well-known that ignoring the informativeness of the cluster size can result in biased parameter estimates. In this article, we consider regression analysis of clustered interval-censored data with informative cluster size with the focus on semiparametric methods. For the problem, two approaches are presented and investigated. One is a within-cluster resampling procedure and the other is a weighted estimating equation approach. Unlike previously published methods, the new approaches take into account cluster sizes and heterogeneous correlation structures without imposing strong parametric assumptions. A simulation experiment is carried out to evaluate the performance of the proposed approaches and indicates that they perform well for practical situations. The approaches are applied to a lymphatic filariasis study that motivated this study.

scholarly journals Regression analysis of clustered interval-censored failure time data

2012 ◽  
Author(s):  
◽  
Junlong Li
Keyword(s):  
Regression Analysis ◽  
Failure Time ◽  
Interval Censoring ◽  
Maximum Likelihood Estimates ◽  
Marginal Model ◽  
Failure Time Data ◽  
Time Data ◽  
Regression Parameters ◽  
Interval Censored ◽  
Clustered Failure Time Data

Clustered failure time data occur when the failure times of interest are clustered into small groups, while interval censoring occurs when the event of interest cannot be observed directly and is only known to have occurred over a time interval. Clustered failure time data often arise together with interval-censoring, which leads to the clustered interval-censored failure time data. In this dissertation, we will focus on the regression analysis of such data. In the first part of the dissertation, a regression analysis under a Cox frailty model is discussed by employing a sieve estimation procedure. In particular, a two-step algorithm is developed for the regression parameter estimation and the asymptotic properties of the resulting sieve maximum likelihood estimates are established. The second part of this dissertation proposes an estimating equation-based approach for the additive hazards model. A major advantage of the proposed method is that it does not involve estimation of any baseline hazard function. Both asymptotic and finite sample properties of the proposed estimates of regression parameters are established and the method is illustrated by the data arising from a lymphatic filariasis study. The last part of the dissertation considers the regression analysis of the same type of data in the context of the linear transformation models. For the inference about the regression parameters, a marginal model approach based on within-cluster resampling (WCR) method is proposed and its large sample properties are also established.

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