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

Abstract

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.

[Retracted] Design of Regression Model for MultiParameter Evaluation of English Pronunciation Quality

“…Its main content is to establish an approximate mathematical expression empirical formula of the correlation between variables by using the least square method based on the observed test data; Te validity of the empirical formula is verifed by the correlation test; Te established efective empirical formula is applied to predict and control. Te practice has proved that using the regression analysis method in economic work is an efective scientifc method for better understanding the relationship between economic phenomena, mastering economic laws, and carrying out economic prediction and control [5,6]. As a global language, English has greatly facilitated the communication of people all over the world.…”

A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes

“…In the current framework, if a unit drops out from a cluster before the failure event, the failure time is right‐censored at the drop‐out time. Depending on the failure event of interest, such censoring may be informative (Yu et al., 2022), and it is of interest to develop joint models for the censoring and failure times. Finally, modifications to the proposed EM algorithm are needed to attain computational scalability in light of applying our methods to data derived from large observational settings.…”