Marginal regression analysis of recurrent events with coarsened censoring times

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1113-1122 ◽  
Author(s):  
X. Joan Hu ◽  
Rhonda J. Rosychuk
Keyword(s):  
Regression Analysis ◽  
Recurrent Events ◽  
Marginal Regression

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.

scholarly journals Kadar Mineral Tembaga dan Besi Dalam Bulu Serta Kaitannya Dengan Kasus Anemia Pada Kambing

2008 ◽  
Vol 8 (1) ◽  
pp. 9-15
Author(s):  
Sugito Sugito
Keyword(s):  
Regression Analysis ◽  
Normal Level ◽  
Packed Cell Volume ◽  
Mineral Content ◽  
Jugular Vein ◽  
Average Rate ◽  
Close Correlation ◽  
Blood Samples ◽  
Marginal Regression ◽  
Hair Samples

Mineral content of Cu and Fe in plasma and hair in related to anemia cases on female goatABSTRACT. A research had been done to identify mineral content of Cu and Fe in plasma and hair related to anemia case on female goat. Twenty female local goats at the age of 12-16 months taken from Kecamatan Darussalam and Kecamatan Baitussalam Kabupaten Aceh Besar were applied. The goats taken have the criteria of hemoglobin rate (Hb) and/or Packed Cell Volume (PCV) lower than normal level. Hair and blood were sampled in this research. Hair samples were taken from top tail area, while blood samples were taken from jugular vein. The result indicated that the average rate of Hb was 77 ± 0.7 mg/dl and PCV was 191 ± 1.3%. The average rate of Cu and Fe in plasma was 0.7 ± 0.16 and 67.8 ± 10.3 μg/ml, while in hair was 2.40 ± 0.511 and 315 ± 8.4 μg/g. The average rate of Cu in plasma was categorized as below normal, while the average rate of Fe in the range of marginal. Regression analysis showed that there was close correlation between Hb rates and PCV as well as the correlation of Hb rate and Cu rate in hair. However, there was no close correlation between Hb rate and Fe rate in hair. The conclusion of the research implied that the amount of PCV and Cu rate in hair relevant to the Hb rate in blood, and could subsequently predict anemia case occurring on female goats. 

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