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Activity Number:
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244
- New Advances in the Analysis of Competing Risks Data and Interval Censored Data and Related Topics
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Lifetime Data Science Section
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Abstract #300661
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Presentation 1
Presentation 2
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Title:
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An Ensemble Method for Interval-Censored Time-To-Event Data
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Author(s):
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W. Yao* and H. Frydman and Jeffrey S. Simonoff
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Companies:
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Stern, New York University and New York University and New York University
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Keywords:
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Conditional inference survival forest;
Cox model;
Data-dependent tuning parameters;
Interval-censored data;
Survival data;
Survival tree method
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Abstract:
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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. In this paper 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.
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Authors who are presenting talks have a * after their name.
