Multiple imputation for competing risks survival data via pseudo-observations

Seungbong Han*, Adin Cristian Andrei, Kam Wah Tsui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Competing risks are commonly encountered in biomedical research. Regression models for competing risks data can be developed based on data routinely collected in hospitals or general practices. However, these data sets usually contain the covariate missing values. To overcome this problem, multiple imputation is often used to fit regression models under a MAR assumption. Here, we introduce a multivariate imputation in a chained equations algorithm to deal with competing risks survival data. Using pseudo-observations, we make use of the available outcome information by accommodating the competing risk structure. Lastly, we illustrate the practical advantages of our approach using simulations and two data examples from a coronary artery disease data and hepatocellular carcinoma data.

Original languageEnglish (US)
Pages (from-to)385-396
Number of pages12
JournalCommunications for Statistical Applications and Methods
Volume25
Issue number4
DOIs
StatePublished - Jul 1 2018

Keywords

  • Competing risks
  • Missing data
  • Multiple imputation
  • Pseudo-observations
  • Random forest

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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