An Approach to Determine Sample Size and to Allocate Sample Size for a Specific Region in a Multiregional Trial for Survival (Time-to-Event) Data under Accelerated Failure Time Model

2014 ◽  
Vol 43 (23) ◽  
pp. 4925-4935 ◽  
Author(s):  
Feng-Shou Ko
Keyword(s):  
Sample Size ◽  
Failure Time ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Event Data ◽  
Time To Event ◽  
Time Model ◽  
Time To Event Data ◽  
Accelerated Failure ◽  
Failure Time Model

Bounded influence function based inference in joint modelling of ordinal partial linear model and accelerated failure time model

2016 ◽  
Vol 25 (6) ◽  
pp. 2714-2732
Author(s):  
Arindom Chakraborty
Keyword(s):  
Influence Function ◽  
Failure Time ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Event Data ◽  
Time To Event ◽  
Time Model ◽  
Time To Event Data ◽  
Accelerated Failure ◽  
Failure Time Model

A common objective in longitudinal studies is to characterize the relationship between a longitudinal response process and a time-to-event data. Ordinal nature of the response and possible missing information on covariates add complications to the joint model. In such circumstances, some influential observations often present in the data may upset the analysis. In this paper, a joint model based on ordinal partial mixed model and an accelerated failure time model is used, to account for the repeated ordered response and time-to-event data, respectively. Here, we propose an influence function-based robust estimation method. Monte Carlo expectation maximization method-based algorithm is used for parameter estimation. A detailed simulation study has been done to evaluate the performance of the proposed method. As an application, a data on muscular dystrophy among children is used. Robust estimates are then compared with classical maximum likelihood estimates.

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