scholarly journals The correlated and shared gamma frailty model for bivariate current status data: An illustration for cross-sectional serological data

2009 ◽  
Vol 28 (22) ◽  
pp. 2785-2800 ◽  
Author(s):  
N. Hens ◽  
A. Wienke ◽  
M. Aerts ◽  
G. Molenberghs
Keyword(s):  
Frailty Model ◽  
Current Status Data ◽  
Current Status ◽  
Cross Sectional ◽  
Gamma Frailty ◽  
Serological Data ◽  
Status Data

scholarly journals Estimation of the Distribution of Duration of Breastfeeding from Cross-Sectional Data: Some Methodological Issue

2021 ◽  
Author(s):  
Anup Kumar ◽  
Sachin Kumar ◽  
Jai Kishun ◽  
Uttam Singh ◽  
Pushpraj .
Keyword(s):  
Life Table ◽  
Current Status Data ◽  
Current Status ◽  
Life Table Analysis ◽  
Cross Sectional ◽  
Kaplan Meier ◽  
Table Analysis ◽  
Duration Of Breastfeeding ◽  
Status Data ◽  
The Impact

Background: Duration of breastfeeding is an important health indicator of mother and child. There are various indirect epidemiological methods available to estimate the duration of breastfeeding from cross sectional data. Objective: To estimate the distribution of duration of breastfeeding at national level cross sectional data and compare various available technique. The impact of the sampling frame (ascertain of the individual understudy) is also evaluated. Method: National Family Health Survey (NFHS-IV) data is used. Duration of breastfeeding of only those children who were born before 60 months from survey date were included in the study. The technique of Current Status Data, Life Table Analysis, and Kaplan Meier (KM) estimator is applied to assess the distribution of duration of breastfeeding. Result: The mean estimate is 32.84, 33.14 and 33.64 months by Kaplan Maier Estimator, Current Status Data and Life Table Analysis respectively. The Current Status and Life Table method are better than Kaplan Meier Estimator as it is doesn’t based on recall data and heaping present in the data. Conclusion: One must be very cautions while estimating the various epidemiological parameters from cross section data set. The assumptions of the methodology as per data available should be evaluate. If such data is not available, the available methodology may be modified. Regression analysis based on Current Status data technique may be used to assess the impact of various clinical and epidemiological factors (such as nutrition of mother, health status of mother etc.) on duration of breastfeeding

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).

A frailty model approach for regression analysis of multivariate current status data

2009 ◽  
Vol 28 (27) ◽  
pp. 3424-3436 ◽  
Author(s):  
Man-Hua Chen ◽  
Xingwei Tong ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Frailty Model ◽  
Current Status Data ◽  
Current Status ◽  
Status Data ◽  
Model Approach

Regression analysis of informative current status data with the additive hazards model

2014 ◽  
Vol 21 (2) ◽  
pp. 241-258 ◽  
Author(s):  
Shishun Zhao ◽  
Tao Hu ◽  
Ling Ma ◽  
Peijie Wang ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Current Status Data ◽  
Current Status ◽  
Additive Hazards Model ◽  
Additive Hazards ◽  
Hazards Model ◽  
Status Data
Sign in / Sign up

Export Citation Format

Share Document