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Kernel-Based Visual Hazard Comparison (kbVHC): a Simulation-Free Diagnostic for Parametric Repeated Time-to-Event

Sebastiaan C Goulooze1, Pyry A J Välitalo1, Catherijne A J Knibbe1,2

  • 1Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands.

The AAPS Journal
|November 29, 2017
PubMed
Summary

This study introduces a new simulation-free diagnostic tool, the kernel-based visual hazard comparison (kbVHC), for repeated time-to-event (RTTE) models. The kbVHC effectively assesses parametric model accuracy without needing complex simulations, improving clinical event analysis.

Keywords:
model diagnosticsnon-linear mixed effect modelspharmacodynamicspharmacometricsrepeated time-to-event models

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Survival Analysis

Background:

  • Parametric repeated time-to-event (RTTE) models are crucial for analyzing recurrent clinical events.
  • Traditional diagnostic methods often rely on simulations, which are challenging with dose titration or informative dropout.
  • There is a need for robust, simulation-free diagnostic tools for RTTE models.

Purpose of the Study:

  • To introduce and evaluate the kernel-based visual hazard comparison (kbVHC), a novel simulation-free diagnostic tool for parametric RTTE models.
  • To assess the ability of kbVHC to determine if the mean predicted hazard rate adequately approximates the true hazard rate.
  • To compare the performance of kbVHC against existing methods like Kaplan-Meier VPC.

Main Methods:

  • The kbVHC compares the predicted hazard rate from a parametric RTTE model to a non-parametric kernel-smoothed hazard estimate.
  • Kernel bandwidth is adaptively selected based on a bootstrap coefficient of variation (CV) target (CVtarget).
  • The tool was evaluated using simulations with varying subject numbers, hazard rates, CVtarget values, and hazard models (Weibull, Gompertz, circadian-varying).

Main Results:

  • kbVHC successfully distinguished between Weibull and Gompertz hazard models, even at low event rates (<2 events/subject).
  • The tool demonstrated higher sensitivity than Kaplan-Meier VPC in detecting circadian hazard variations.
  • The kernel estimator component of kbVHC can be used for preliminary hazard rate shape exploration.

Conclusions:

  • The kernel-based visual hazard comparison (kbVHC) offers a valuable simulation-free approach for diagnosing parametric RTTE models.
  • kbVHC enhances the reliability of RTTE model diagnostics, particularly in complex clinical scenarios.
  • This method provides a more sensitive and flexible alternative for hazard rate assessment in survival analysis.