scholarly journals An approach for jointly modeling multivariate longitudinal measurements and discrete time-to-event data

2010 ◽  
Vol 4 (3) ◽  
pp. 1517-1532 ◽  
Author(s):  
Paul S. Albert ◽  
Joanna H. Shih
Keyword(s):  
Discrete Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Longitudinal Measurements

Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

2018 ◽  
Vol 61 (3) ◽  
pp. 514-534 ◽  
Author(s):  
Rachel Heyard ◽  
Jean‐François Timsit ◽  
Wafa Ibn Essaied ◽  
Leonhard Held ◽  
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Prediction Models ◽  
Event Data ◽  
Clinical Prediction ◽  
Time To Event ◽  
Clinical Prediction Models ◽  
Time To Event Data

Gerhard Tutz and Matthias Schmid. Modeling Discrete Time-to-Event Data. Heidelberg, Springer.

Biometrics ◽  
2018 ◽  
Vol 74 (1) ◽  
pp. 378-379
Author(s):  
Jan Beyersmann
Keyword(s):  
Discrete Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Modeling Discrete Time-to-Event Data

2016 ◽  
Author(s):  
Gerhard Tutz ◽  
Matthias Schmid
Keyword(s):  
Discrete Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Bayesian Joint Modeling of Longitudinal Measurements and Time-to-Event Data Using Robust Distributions

2014 ◽  
Vol 24 (4) ◽  
pp. 834-855 ◽  
Author(s):  
T. Baghfalaki ◽  
M. Ganjali ◽  
R. Hashemi
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Longitudinal Measurements

scholarly journals Analyzing Response Times and Other Types of Time-to-Event Data Using Event History Analysis: A Tool for Mental Chronometry and Cognitive Psychophysiology

i-Perception ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 204166952097867
Author(s):  
Sven Panis ◽  
Filipp Schmidt ◽  
Maximilian P. Wolkersdorfer ◽  
Thomas Schmidt
Keyword(s):  
Discrete Time ◽  
Event History Analysis ◽  
Masked Priming ◽  
Event History ◽  
Event Data ◽  
Time To Event ◽  
Advantages And Disadvantages ◽  
Time To Event Data ◽  
History Analysis ◽  
Speed Accuracy

In this Methods article, we discuss and illustrate a unifying, principled way to analyze response time data from psychological experiments—and all other types of time-to-event data. We advocate the general application of discrete-time event history analysis (EHA) which is a well-established, intuitive longitudinal approach to statistically describe and model the shape of time-to-event distributions. After discussing the theoretical background behind the so-called hazard function of event occurrence in both continuous and discrete time units, we illustrate how to calculate and interpret the descriptive statistics provided by discrete-time EHA using two example data sets (masked priming, visual search). In case of discrimination data, the hazard analysis of response occurrence can be extended with a microlevel speed-accuracy trade-off analysis. We then discuss different approaches for obtaining inferential statistics. We consider the advantages and disadvantages of a principled use of discrete-time EHA for time-to-event data compared to (a) comparing means with analysis of variance, (b) other distributional methods available in the literature such as delta plots and continuous-time EHA methods, and (c) only fitting parametric distributions or computational models to empirical data. We conclude that statistically controlling for the passage of time during data analysis is equally important as experimental control during the design of an experiment, to understand human behavior in our experimental paradigms.

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