Nonparametric analysis of competing risks data with event category missing at random

Natalia A. Gouskova, Feng Chang Lin, Jason P. Fine

Research output: Research - peer-reviewArticle

Abstract

In competing risks setup, the data for each subject consist of the event time, censoring indicator, and event category. However, sometimes the information about the event category can be missing, as, for example, in a case when the date of death is known but the cause of death is not available. In such situations, treating subjects with missing event category as censored leads to the underestimation of the hazard functions. We suggest nonparametric estimators for the cumulative cause-specific hazards and the cumulative incidence functions which use the Nadaraya–Watson estimator to obtain the contribution of an event with missing category to each of the cause-specific hazards. We derive the propertied of the proposed estimators. Optimal bandwidth is determined, which minimizes the mean integrated squared errors of the proposed estimators over time. The methodology is illustrated using data on lung infections in patients from the United States Cystic Fibrosis Foundation Patient Registry.

LanguageEnglish (US)
Pages104-113
Number of pages10
JournalBiometrics
Volume73
Issue number1
DOIs
StatePublished - Mar 1 2017

Fingerprint

Missing at Random
Competing Risks
risk analysis
death
Hazards
cystic fibrosis
lungs
incidence
infection
methodology
Cystic Fibrosis
Registries
Cause of Death
Lung
Incidence
Infection
Cause-specific Hazard
Estimator
Bandwidth
Nadaraya-Watson Estimator

Keywords

  • Competing risks
  • Cystic fibrosis
  • Missing event category
  • Nadaraya–Watson estimator
  • Nonparametric estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Nonparametric analysis of competing risks data with event category missing at random. / Gouskova, Natalia A.; Lin, Feng Chang; Fine, Jason P.

In: Biometrics, Vol. 73, No. 1, 01.03.2017, p. 104-113.

Research output: Research - peer-reviewArticle

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