Hierarchical Nonlinear Mixed Effects Modeling: Defining Post-Radiation Therapy Relapse in Prostate Cancer Patients

Abstract

During this grant period, a linear-quadratic random effects model of the profile of PSA levels in men following radiation therapy for prostate cancer was developed. It can be used to predict, for a new patient, the expected trajectory of future PSA levels, and the probability of biochemical failure. The model, which includes terms for initial PSA, post- treatment PSA nadir, time of nadir, and future PSA level, also allows us to update these predictions as new PSA measurements on the patient are collected. We show that our method has some advantages over the widely used definition of biochemical failure as three consecutive rises in post-nadir PSA level.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2003
Accession Number
ADA418391

Entities

People

  • Alexandra L. Hanlon

Organizations

  • Fox Chase Cancer Center

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Biomedical Research
  • Classification
  • Diseases And Disorders
  • Measurement
  • Models
  • Neoplasms
  • Oncology
  • Predictive Modeling
  • Probability
  • Prostate
  • Prostate Cancer
  • Radiation
  • Radiation Oncology
  • Trajectories

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Educational Psychology
  • Oncology