Exploring Migration Data Using Interval-Censored Time-to-Event Models

2008 ◽  
Vol 72 (5) ◽  
pp. 1211-1219 ◽  
Author(s):  
John Fieberg ◽  
Glenn Delgiudice
Keyword(s):  
Time To Event ◽  
Migration Data ◽  
Interval Censored ◽  
Event Models

scholarly journals Variability of Zika Virus Incubation Period in Humans

2018 ◽  
Vol 5 (11) ◽  
Author(s):  
Toscane Fourié ◽  
Gilda Grard ◽  
Isabelle Leparc-Goffart ◽  
Sébastien Briolant ◽  
Albin Fontaine
Keyword(s):  
Confidence Interval ◽  
Incubation Period ◽  
Median Time ◽  
Censored Data ◽  
Zika Virus ◽  
Time To Event ◽  
Tropical Countries ◽  
Period Distribution ◽  
Interval Censored ◽  
Event Models

Abstract Zika virus (ZIKV) has recently emerged in numerous tropical countries worldwide. In this study, we estimated ZIKV incubation period distribution using time-to-event models adapted to interval-censored data based on declared date of travels from 123 symptomatic travelers returning from areas with active ZIKV transmission. The median time and 95th percentile of ZIKV incubation period was estimated to 6.8 days (95% confidence interval [CI], 5.8–7.7 days) and 15.4 days (95% CI, 12.7–19.7 days), respectively. Determining the incubation period for ZIKV is beneficial to improve protection guidelines.

A comparison of estimators to evaluate the discriminatory power of time-to-event models

2012 ◽  
Vol 31 (23) ◽  
pp. 2588-2609 ◽  
Author(s):  
Matthias Schmid ◽  
Sergej Potapov
Keyword(s):  
Discriminatory Power ◽  
Time To Event ◽  
Event Models

scholarly journals Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

2011 ◽  
Vol 53 (1) ◽  
pp. 88-112 ◽  
Author(s):  
Rotraut Schoop ◽  
Jan Beyersmann ◽  
Martin Schumacher ◽  
Harald Binder
Keyword(s):  
Competing Risks ◽  
Predictive Accuracy ◽  
Time To Event ◽  
Event Models

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 Hydrothermal-time-to-event models for seed germination

2018 ◽  
Vol 101 ◽  
pp. 129-139 ◽  
Author(s):  
Andrea Onofri ◽  
Paolo Benincasa ◽  
Mohsen B. Mesgaran ◽  
Christian Ritz
Keyword(s):  
Seed Germination ◽  
Hydrothermal Time ◽  
Time To Event ◽  
Event Models
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