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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Interval censored recursive forests.

Hunyong Cho1, Nicholas P Jewell2, Michael R Kosorok3

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

We introduce interval censored recursive forests (ICRF), a novel iterative tree ensemble method for interval censored survival data. This approach improves prediction accuracy and addresses bias in survival analysis.

Keywords:
interval censored datakernel-smoothingquasi-honestyrandom forestself-consistencysurvival analysis

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Area of Science:

  • Statistics
  • Machine Learning
  • Survival Analysis

Background:

  • Interval censored survival data presents unique challenges for traditional survival analysis methods.
  • Existing tree-based methods often suffer from splitting bias when handling interval censored data.

Purpose of the Study:

  • To propose a novel nonparametric regression estimator, interval censored recursive forests (ICRF), for interval censored survival data.
  • To address the splitting bias inherent in existing tree-based methods for this data type.

Main Methods:

  • Developed an iterative tree ensemble method (ICRF) for interval censored survival data.
  • Implemented consistent splitting rules and applied kernel-smoothing for improved estimation.
  • Monitored convergence using out-of-bag samples for self-consistent survival estimate updates.

Main Results:

  • The proposed ICRF method demonstrates uniform consistency.
  • ICRF achieves high prediction accuracy in both simulated datasets and real-world applications.
  • Successful application to avalanche and national mortality data confirms its utility.

Conclusions:

  • ICRF offers a robust and accurate solution for interval censored survival data analysis.
  • The method effectively mitigates splitting bias, outperforming existing tree-based approaches.
  • An R package 'icrf' is available for practical implementation.