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Multiple imputation for competing risks survival data via pseudo-observations
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Type
Academic journal
Author
Journal
한국통계학회 CSAM(Communications for Statistical Applications and Methods) Vol.25 No.4 KCI Accredited Journals SCOPUS
Published
2018.7
Pages
385 - 396 (12page)

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Multiple imputation for competing risks survival data via pseudo-observations
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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.
#competing risks #missing data #multiple imputation #pseudo-observations #random for-est

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