Estimates of clinically useful measures in competing risks survival analysis

Federico Ambrogi, Elia Biganzoli, Patrizia Boracchi

Research output: Contribution to journalArticlepeer-review

Abstract

Competing risks occur frequently in follow-up clinical studies. To assess treatment or covariate effects, measures of clinical impact based on crude cumulative incidence should be considered, such as relative risks or the absolute risk reduction. In this work, transformation models through suitable link functions provide a straightforward approach to obtain point and interval estimates of such measures. An extension of the Klein and Andersen proposal, based on pseudo-values, is considered. Non-additive effects were tested by interactions between baseline (spline function on time) and covariates. The methods are applied to the evaluation of the impact of axillary lymph node nanometastases on metastatic relapse of breast cancer patients. Further, a literature data set on prostate cancer was used for illustration.

Original languageEnglish
Pages (from-to)6407-6425
Number of pages19
JournalStatistics in Medicine
Volume27
Issue number30
DOIs
Publication statusPublished - Dec 2008

Keywords

  • Clinically useful measures
  • Competing risks
  • Generalized estimating equations
  • Pseudo-values

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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