PENALIZED LOG-LIKELIHOOD ESTIMATION FOR COX-FRAILTY MODEL WITH NONINFORMATIVE BIVARIATE CURRENT STATUS DATA

2021 ◽  
pp. 16-27
Author(s):  
Alhassan Faisal
Keyword(s):  
Variance Estimation ◽  
Proportional Hazards ◽  
Likelihood Estimation ◽  
Frailty Model ◽  
Cox Proportional Hazards ◽  
Current Status Data ◽  
Current Status ◽  
Hazard Functions ◽  
Failure Times ◽  
Status Data

A Penalized Maximum Likelihood Estimation (PMLE) procedure is proposed for Cox proportional hazards frailty model with noninformative bivariate current status data. An integrated splines (I-splines) was used to approximate the two unknown baseline cumulative hazard functions of the failure times. The one-parameter gamma frailty distribution was used to model the correlation between the two failure times. An easy to implement computational algorithm is proposed to estimate the regression and splines parameters. Bayesian technique as proposed by Wahba (1983) was employed for the variance estimation. The statistical properties of the estimated parameters were studied through extensive simulation and it was observed that the PMLEs were consistent, asymptotically normal and efcient. In addition, the estimators were robust to the choice of knots, censoring rates and type of frailty distribution used. The proposed methodology is further demonstrated through the analysis of the tumorigenicity experiment data by Lindsey and Ryan (1994).

Social and economic factors related to breast-feeding durations in Tanzania

1996 ◽  
Vol 28 (3) ◽  
pp. 347-354 ◽  
Author(s):  
P. R. Andrew Hinde ◽  
Akim J. Mturi
Keyword(s):  
Urban Area ◽  
Breast Feeding ◽  
Proportional Hazards ◽  
Current Status Data ◽  
Current Status ◽  
Economic Factors ◽  
Region Of Residence ◽  
Mother And Child ◽  
Proportional Odds Models ◽  
Status Data

SummarySome social and economic factors related to breast-feeding durations in Tanzania are analysed using current status data taken from the 1991–92 Tanzanian Demographic and Health Survey. Proportional hazards and proportional odds models are estimated. The results show that breast-feeding durations vary according to the region of residence of the mother and child (and whether they are living in a rural or an urban area), the age of the mother at the time of the birth, the order of the birth, and the mother's religion.

Statistical analysis of bivariate interval-censored failure time data

2015 ◽  
Author(s):  
◽  
Qingning Zhou
Keyword(s):  
Statistical Analysis ◽  
Censored Data ◽  
Failure Time ◽  
Current Status Data ◽  
Current Status ◽  
Interval Censored Data ◽  
Time Data ◽  
Failure Times ◽  
Interval Censored ◽  
Status Data

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Interval-censored failure time data arise when the failure time of interest in a survival study is not exactly observed but known only to fall within some interval. One area that often produces such data is medical studies with periodic follow-ups, in which the medical condition of interest such as the onset of a disease is only known to occur between two adjacent examination times. An important special case of intervalcensored data is current status data which arise when each study subject is observed only once and the only information available is whether the failure event of interest has occurred or not by the observation time. The areas that often yield such data include tumorigenicity experiments and cross-sectional studies. Sometimes we refer to current status data as case I interval-censored data, and the general case as case II interval-censored data. The analysis of both case I and case II interval-censored data has recently attracted a great deal of attention and many procedures have been proposed for various issues related to it. However, there are still a number of problems that remain unsolved or lack approaches that are simpler, more efficient and apply to more general situations compared to the existing ones. This is especially the case for multivariate intervalcensored data which arise if there are multiple failure times of interest and all of them suffer intervalcensoring. This dissertation focuses on the statistical analysis for bivariate interval-censored data, including regression analysis, model selection and estimation of the association between failure times.

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