scholarly journals Bayesian analysis of type-I right censored data using the 3-component mixture of Burr distributions

2016 ◽  
Vol 23 (5) ◽  
pp. 2374-2390
Author(s):  
M. Tahir ◽  
M. Aslam ◽  
Z. Hussain
Keyword(s):  
Bayesian Analysis ◽  
Censored Data ◽  
Type I ◽  
Component Mixture ◽  
Right Censored Data ◽  
Burr Distributions

An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring

2016 ◽  
Vol 27 (4) ◽  
pp. 1230-1239 ◽  
Author(s):  
Julien Péron ◽  
Marc Buyse ◽  
Brice Ozenne ◽  
Laurent Roche ◽  
Pascal Roy
Keyword(s):  
Censored Data ◽  
Type I Error ◽  
Standard Procedure ◽  
Survival Function ◽  
Control Group ◽  
Pairwise Comparisons ◽  
Type I ◽  
Time To Event ◽  
Right Censored Data ◽  
Higher Power

Generalized pairwise comparisons have been proposed to permit a comprehensive assessment of several prioritized outcomes between two groups of observations. This procedure estimates Δ, the net chance of a better outcome with treatment than with control by comparing the patients outcomes among all possible pairs taking one patient from the treatment group and one patient from the control group. For time to event outcomes, the standard procedure of generalized pairwise comparisons is analogous to the Gehan’s modification of the Mann-Whitney test which is biased in presence of censored observation and less powerful than Efron’s modification of this test. We adapt Efron’s modification to generalized pairwise comparisons. We show how a pairwise contribution to Δ can be calculated from the estimates of the survival function in the presence of right-censored data. We performed a simulation study to assess the bias, the type I error and the power of the new procedure. The estimate of Δ with the new procedure is only slightly biased even in presence of heavy censoring. We also show how this bias can be corrected when only one time-to-event outcome is analyzed. The new procedure has higher power in most cases compared to the standard procedure.

scholarly journals Bayesian Analysis of the 3-Component Mixture of Exponential Distribution Assuming the Non-Informative Priors

2015 ◽  
Vol 38 (2) ◽  
pp. 431-452
Author(s):  
Muhammad Tahir ◽  
Muhammad Aslam
Keyword(s):  
Bayesian Analysis ◽  
Exponential Distribution ◽  
Loss Function ◽  
Right Censoring ◽  
Type I ◽  
Bayes Estimators ◽  
Unknown Parameters ◽  
Component Mixture ◽  
Squared Error Loss Function ◽  
Termination Time

Bayesian analysis of the 3-component mixture of an Exponential distribution under type-I right censoring scheme is considered in this paper. The Bayes estimators and posterior risks for the unknown parameters are derived under squared error loss function, precautionary loss function and DeGroot loss function assuming the non-informative (uniform and Jeffreys') priors. The Bayes estimators and posterior risks are viewed as a function of the test termination time. A simulation study is given to highlight and compare the properties of the Bayes estimates.

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