Meta-analysis of full ROC curves using bivariate time-to-event models for interval-censored data

2017 ◽  
Vol 9 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Annika Hoyer ◽  
Stefan Hirt ◽  
Oliver Kuss
Keyword(s):  
Censored Data ◽  
Meta Analysis ◽  
Roc Curves ◽  
Interval Censored Data ◽  
Time To Event ◽  
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.

scholarly journals An ensemble method for interval-censored time-to-event data

Biostatistics ◽  
2019 ◽  
Author(s):  
Weichi Yao ◽  
Halina Frydman ◽  
Jeffrey S Simonoff
Keyword(s):  
Censored Data ◽  
Cox Model ◽  
Conditional Inference ◽  
Interval Censored Data ◽  
Time To Event ◽  
Data Set ◽  
Interval Censored ◽  
Survival Tree ◽  
True Relationship ◽  
Tree Method

Summary Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this article, we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that the tuning parameters have a non-negligible effect on the survival forest performance and guidance is provided on how to tune the parameters in a data-dependent way to improve the overall performance of the method. Using Monte Carlo simulations, we find that the proposed survival forest is at least as effective as a survival tree method when the underlying model has a tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and outperforms the survival tree method and Cox model when the true relationship is nonlinear. We illustrate the application of the method on a tooth emergence data set.

The Cox-Aalen model for left-truncated and mixed interval-censored data

Statistics ◽  
2019 ◽  
Vol 53 (5) ◽  
pp. 1152-1167 ◽  
Author(s):  
Pao-sheng Shen ◽  
Li Ning Weng
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Aalen Model ◽  
Interval Censored
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