Analysing adverse events by time-to-event models: the CLEOPATRA study

2016 ◽  
Vol 15 (4) ◽  
pp. 306-314 ◽  
Author(s):  
Tanja Proctor ◽  
Martin Schumacher
Keyword(s):  
Adverse Events ◽  
Time To Event ◽  
Event Models

scholarly journals The association between the pattern of change in N-terminal pro-B-type natriuretic peptide and short-term outcomes in children undergoing surgery for congenital heart disease

2020 ◽  
Author(s):  
Haiqing Zheng ◽  
Yanqin Cui ◽  
Kuanrong Li ◽  
Jiexin Zhang ◽  
Jiangbo Qu ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Adverse Events ◽  
Natriuretic Peptide ◽  
Congenital Heart ◽  
Latent Class ◽  
Mode Of Delivery ◽  
Time To Event ◽  
Short Term ◽  
Event Models

Abstract OBJECTIVES This study aimed to determine whether changes in perioperative N-terminal pro-B-type natriuretic peptide (NT-proBNP) are associated with short-term outcomes in children undergoing surgery for congenital heart disease (CHD). METHODS We retrospectively included 873 consecutive children with CHD after cardiac surgery. NT-proBNP concentrations were collected from each child prior to and at 1, 12, 36 and 72 h after surgery. The patients had postsurgical follow-ups at 30, 90 and 180 days. The end point was postoperative composite adverse events. RESULTS The patients were classified into 3 groups using joint latent class mixture time-to-event models: (i) relatively stable (86.7%), (ii) decreasing (7.2%) and (iii) increasing (6.1%). In total, 257 (29.4%) adverse events occurred. The joint latent class mixture time-to-event models showed that increasing NT-proBNP was strongly associated with adverse events, with adjusted hazard ratio of 2.33 (95% confidence interval 1.52–3.60). Multinomial logistic regression showed that the variables associated with the pattern of change were age, weight at surgery, mode of delivery and cardiopulmonary bypass time. CONCLUSIONS The pattern of dynamic postsurgical changes in NT-proBNP may facilitate outcome stratification and identification of a high risk for adverse events.

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

Statistical issues in the analysis of adverse events in time-to-event data

2016 ◽  
Vol 15 (4) ◽  
pp. 297-305 ◽  
Author(s):  
Arthur Allignol ◽  
Jan Beyersmann ◽  
Claudia Schmoor
Keyword(s):  
Adverse Events ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Statistical Issues
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