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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Testing for center effects on survival and competing risks outcomes using pseudo-value regression.

Yanzhi Wang1, Brent R Logan2

  • 1Division of Research Services/Department of Medicine, University of Illinois College of Medicine at Peoria, 1 Illini Dr., Peoria, IL, 61605, USA. yzwang@uic.edu.

Lifetime Data Analysis
|July 7, 2018
PubMed
Summary

Researchers can now test for center effects directly on survival probabilities using pseudo-value regression. This new method offers a powerful alternative to hazard function tests, especially for time-varying cluster effects in multicenter studies.

Keywords:
Clustered time to event dataCumulative incidenceGeneralized linear mixed modelPseudo-value regression

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Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Epidemiology

Background:

  • Multicenter studies often exhibit cluster effects, causing correlations in outcomes within centers.
  • Existing methods for cluster effect testing primarily focus on the hazard function for time-to-event data.
  • Researchers may need to analyze other quantities like survival probabilities or cumulative incidence directly.

Purpose of the Study:

  • To propose a novel statistical test for detecting center effects directly on specific outcome measures.
  • To evaluate the performance of this new test in survival and competing risks analyses.
  • To provide an alternative to hazard-based tests, particularly when center effects are time-varying.

Main Methods:

  • Development of a new test statistic based on pseudo-value regression.
  • Derivation of the asymptotic properties of the proposed test statistic.
  • Simulation studies were conducted to assess the test's performance in various scenarios.
  • Application of the test to a real-world multicenter registry study of hematopoietic cell transplantation outcomes.

Main Results:

  • The proposed pseudo-value regression test effectively detects center effects on quantities of interest.
  • Simulation studies demonstrated the test's validity and performance in both survival and competing risks settings.
  • The new test showed potential for greater power than hazard-based tests when center effects vary over time.

Conclusions:

  • Pseudo-value regression offers a flexible approach for testing center effects directly on survival probabilities and cumulative incidence.
  • This method provides a valuable tool for analyzing clustered data in multicenter studies, complementing existing hazard-based approaches.
  • The proposed test is applicable to complex outcomes in real-world clinical research, such as post-transplantation survival.