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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Joint modelling of longitudinal and competing risks data.

P R Williamson1, R Kolamunnage-Dona, P Philipson

  • 1Centre for Medical Statistics and Health Evaluation, University of Liverpool, Shelley's Cottage, Brownlow Street, Liverpool L69 3GS, UK. prw@liv.ac.uk

Statistics in Medicine
|October 1, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new joint modeling method for longitudinal and survival data with competing risks. Lamotrigine (LTG) remains the preferred anti-epileptic drug (AED) over carbamazepine (CBZ), even after accounting for drug titration rates.

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Last Updated: Jun 29, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Pharmacovigilance

Background:

  • Joint modeling of longitudinal and survival data is crucial for understanding treatment effects.
  • Existing methods often lack the ability to handle multiple failure types (competing risks).
  • The SANAD trial provides valuable data on anti-epileptic drugs (AEDs) like lamotrigine (LTG) and carbamazepine (CBZ).

Purpose of the Study:

  • To extend joint modeling methodology to accommodate competing risks in time-to-event outcomes.
  • To investigate the impact of drug titration rates on the comparative effectiveness of LTG and CBZ.
  • To determine the optimal anti-epileptic drug (AED) based on treatment failure and adverse events.

Main Methods:

  • Developed a joint model incorporating a cause-specific hazards sub-model for competing risks.
  • Modeled latent associations between longitudinal measurements and each failure cause.
  • Applied the method to SANAD trial data, adjusting for differential titration rates of LTG and CBZ.

Main Results:

  • The beneficial effect of LTG on adverse events leading to withdrawal was maintained and slightly increased when calibrated for dose.
  • Adjusting for LTG's titration rate relative to CBZ did not alter LTG's effect on seizure control failures.
  • The analysis confirms LTG as the preferred AED based on the evaluated outcomes.

Conclusions:

  • The extended joint modeling approach effectively handles competing risks in longitudinal and survival data.
  • LTG demonstrates a favorable profile regarding adverse events and seizure control compared to CBZ, irrespective of titration differences.
  • This analysis supports LTG as the AED of choice for epilepsy management.