Likelihood-based and marginal inference methods for recurrent event data with covariate measurement error

2012 ◽  
Vol 40 (3) ◽  
pp. 530-549 ◽  
Author(s):  
Grace Y. Yi ◽  
Jerald F. Lawless
Keyword(s):  
Measurement Error ◽  
Recurrent Event ◽  
Event Data ◽  
Recurrent Event Data ◽  
Covariate Measurement Error ◽  
Inference Methods ◽  
Marginal Inference

scholarly journals Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Hsiang Yu ◽  
Yu-Jen Cheng ◽  
Ching-Yun Wang
Keyword(s):  
Measurement Error ◽  
Recurrent Events ◽  
Semiparametric Regression ◽  
Informative Censoring ◽  
Recurrent Event ◽  
Occurrence Rate ◽  
Event Data ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Cancer Trial

AbstractRecurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate for both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. We propose two corrected approaches based on different ideas, and we show that they are numerically identical when estimating the regression parameters. The asymptotic properties of the proposed estimators are established, and the finite sample performance is examined via simulations. The proposed methods are applied to the Nutritional Prevention of Cancer trial for assessing the effect of the plasma selenium treatment on the recurrence of squamous cell carcinoma.

scholarly journals Penalized survival models for the analysis of alternating recurrent event data

Biometrics ◽  
2019 ◽  
Vol 76 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Lili Wang ◽  
Kevin He ◽  
Douglas E. Schaubel
Keyword(s):  
Recurrent Event ◽  
Survival Models ◽  
Event Data ◽  
Recurrent Event Data

Semiparametric methods for regression analysis of panel count data and mixed panel count data

2017 ◽  
Author(s):  
◽  
Guanglei Yu
Keyword(s):  
Regression Analysis ◽  
Simulation Study ◽  
Count Data ◽  
Recurrent Events ◽  
Recurrent Event ◽  
Mixed Data ◽  
Panel Count Data ◽  
Event Data ◽  
Recurrent Event Data ◽  
Recurrent Event Process

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recurrent event data and panel count data are two common types of data that have been studied extensively in event history studies in literature. By recurrent event data, we mean that subjects are observed continuously in the follow-up study and thus occurrence times of recurrent events of interest are available. For panel count data, subjects are monitored periodically at discrete observation times and thus only numbers of recurrent events between two subsequent observations are recorded. In addition, one may face mixed panel count data in practice, which are the mixture of recurrent event data and panel count data. They arise when each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points otherwise, or only at some discrete time points. That is, these mixed data provide complete or incomplete information on the recurrent event process over different time periods for different subjects. It is well-known that in panel count data, the observation process may carry information on the underlying recurrent event process and the censoring may also be dependent in practice. Under such circumstance, the first part of this dissertation will discuss regression analysis of panel count data with informative observations and drop-outs. For the problem, a general means model is presented that can allow both additive and multiplicative effects of covariates on the underlying recurrent event process. In addition, the proportional rates model and the accelerated failure time model are employed to describe the covariate effects on the observation process and the dropout or follow-up process, respectively. For estimation of regression parameters, some estimating equation-based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for the estimation of the covariance matrix of the proposed estimator and a model checking procedure is also provided. The results from an extensive simulation study indicate that the proposed methodology works well for practical situations and it is applied to a motivated set of real data from the Childhood Cancer Survivor Study (CCSS) given in Section 1.1.2.2. In the second part of this dissertation, we will consider regression analysis of mixed panel count data. One major problem in the statistical inference on the mixed data is to combine these two different types of data structures. Since panel count data can be viewed as interval-censored recurrent event data with exact occurrence times of events of interest unobserved or missing, they may be augmented by filling in those missing data by imputation. Then the mixed data can be converted to recurrent event data on which the existing statistical inference method can be easily implemented. Motivated by this, a multiple imputation-based estimation approach is proposed. A simulation study is conducted to study the finite-sample properties of the proposed methodology and it shows that the proposed method is more efficient than the existing method. Also, an illustrative example from the CCSS is provided. The third part of this dissertation still considers regression analysis of mixed panel count data but in the presence of a dependent terminal event, which precludes further occurrence of either recurrent events of interest or observations. For this problem, we present a marginal modeling approach which acknowledges the fact that there will be no more recurrent events after the terminal event and leaves the correlation structure unspecified. To estimate the parameters of interest, an estimating equation-based procedure is developed and the inverse probability of survival weighting technique is used. Asymptotic properties of proposed estimators are also established and finite-sample properties are assessed in a simulation study. We again apply this proposed methodology to the CCSS. In the last part of this dissertation, we will discuss some work directions of the future research.

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