scholarly journals Semiparametric regression analysis for time-to-event marked endpoints in cancer studies

Biostatistics ◽  
2013 ◽  
Vol 15 (3) ◽  
pp. 513-525 ◽  
Author(s):  
C. Hu ◽  
A. Tsodikov
Keyword(s):  
Regression Analysis ◽  
Semiparametric Regression ◽  
Time To Event ◽  
Cancer Studies

scholarly journals Semiparametric regression for discrete time-to-event data

2018 ◽  
Vol 18 (3-4) ◽  
pp. 322-345 ◽  
Author(s):  
Moritz Berger ◽  
Matthias Schmid
Keyword(s):  
Semiparametric Regression ◽  
Outcome Variable ◽  
Nonlinear Functions ◽  
Time To Event ◽  
Regression Methods ◽  
Time To Event Data ◽  
Duration Of Unemployment ◽  
Event Models ◽  
Analyse Data ◽  
Event Times

Abstract: Time-to-event models are a popular tool to analyse data where the outcome variable is the time to the occurrence of a specific event of interest. Here, we focus on the analysis of time-to-event outcomes that are either intrinsically discrete or grouped versions of continuous event times. In the literature, there exists a variety of regression methods for such data. This tutorial provides an introduction to how these models can be applied using open source statistical software. In particular, we consider semiparametric extensions comprising the use of smooth nonlinear functions and tree-based methods. All methods are illustrated by data on the duration of unemployment of US citizens.

scholarly journals Semiparametric regression analysis for alternating recurrent event data

2017 ◽  
Vol 37 (6) ◽  
pp. 996-1008 ◽  
Author(s):  
Chi Hyun Lee ◽  
Chiung-Yu Huang ◽  
Gongjun Xu ◽  
Xianghua Luo
Keyword(s):  
Regression Analysis ◽  
Semiparametric Regression ◽  
Recurrent Event ◽  
Event Data ◽  
Recurrent Event Data
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