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stpm2cr: A flexible parametric competing risks model using a direct likelihood approach for the cause-specific

Sarwar Islam Mozumder1, Mark J Rutherford1, Paul C Lambert2

  • 1Department of Health Sciences, University of Leicester, Leicester, UK.

The Stata Journal
|October 12, 2018
PubMed
Summary

This study introduces a direct flexible parametric modeling approach for cause-specific cumulative incidence functions (CIFs) in competing risks analysis. The method enhances computational efficiency and prediction accuracy for prognostic questions.

Keywords:
competing riskscumulative incidence functionflexible parametric modelsst0001stpm2crsubdistribution hazardsurvival analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Competing risks analysis is crucial for understanding event occurrences when multiple outcomes are possible.
  • Cause-specific cumulative incidence functions (CIFs) are key metrics, typically derived from cause-specific hazards (CSH) or subdistribution hazards (SDH).
  • Flexible parametric modeling (FPM) offers a robust framework for survival data analysis.

Purpose of the Study:

  • To propose and implement a direct flexible parametric modeling (FPM) approach for cause-specific cumulative incidence functions (CIFs) within competing risks analysis.
  • To enhance the efficiency and utility of modeling CIFs, particularly for prognostic predictions.
  • To introduce a new estimation command, stpm2cr, for practical application of the proposed methods.

Main Methods:

  • Utilizing the flexible parametric modeling (FPM) framework to directly model cause-specific CIFs.
  • Modeling the (log-cumulative) baseline hazard without requiring numerical integration.
  • Incorporating alternative link functions, such as the logit link, for enhanced flexibility.
  • Introducing the `stpm2cr` estimation command for implementation.

Main Results:

  • The direct FPM approach for CIFs offers computational benefits by avoiding numerical integration.
  • The method facilitates straightforward out-of-sample predictions for prognostic assessments.
  • Demonstrated utility through an illustrative analysis of a Melanoma dataset.

Conclusions:

  • The direct FPM approach provides an efficient and flexible method for analyzing cause-specific cumulative incidence functions in competing risks.
  • This methodology is particularly advantageous for answering prognostic questions and improving prediction accuracy.
  • The new `stpm2cr` command enables practical application and further research in competing risks modeling.