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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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Multi-marker testing based on accelerated failure time models under possible left truncation and competing risks.

Chenxi Li1, Di Wu1, Qing Lu2

  • 1Department of Epidemiology and Biostatistics, Michigan State University, 909 Wilson Road, 48824 MI, United States.

Briefings in Bioinformatics
|April 9, 2026
PubMed
Summary
This summary is machine-generated.

We developed new survival tests for genetic analysis using the accelerated failure time model, offering a robust alternative to the Cox model for complex genetic association studies.

Keywords:
accelerated failure time modelcompeting risksgenetic heterogeneitykernel functionsleft truncationmulti-marker tests

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Area of Science:

  • Genetics
  • Biostatistics
  • Survival Analysis

Background:

  • Kernel-based multi-marker survival tests commonly employ the Cox model.
  • The Cox model's proportional hazards assumption may not hold for long-term survival data.
  • Alternative models are needed for more accurate genetic association studies.

Purpose of the Study:

  • To develop novel multi-marker survival tests for genetic association and interaction.
  • To utilize the accelerated failure time (AFT) model as an alternative to the Cox model.
  • To enhance power and accuracy in genetic association analyses.

Main Methods:

  • Developed novel multi-marker survival tests based on the accelerated failure time model.
  • Tests leverage asymptotic distributions for computational efficiency.
  • Incorporated methods to handle genetic effect heterogeneity, competing risks, and left truncation.
  • Developed small-sample corrections for improved accuracy.

Main Results:

  • The new tests demonstrate strong performance across various scenarios in numerical experiments.
  • The tests are computationally efficient and suitable for long-term follow-up.
  • Methods effectively address heterogeneity, competing risks, and left truncation.

Conclusions:

  • The novel accelerated failure time-based multi-marker survival tests offer a powerful and flexible alternative to Cox model-based approaches.
  • These tests provide practical utility, as demonstrated by their application to Alzheimer's disease genetics.
  • The developed methods improve the accuracy and applicability of genetic association studies in survival analysis.