| When competing risks data arise, information on the actual cause of failure for some sub-jects might be missing. Therefore, cause-specific survival models together with multiple im-putations (MI) have been proposed to analyze such data. Modelling the cumulative incidence function is also of interest, and thus we investigate the quantile regression model together with MI methods as a modelling approach for competing risks data with missing cause of failure. Possible strategies for analyzing such data include the complete case analysis as well as an anal-ysis where the missing causes are classified as an additional failure type. These approaches, however, may produce misleading results in clinical settings. In addition, inverse probability weighting (IPW) methods will increase errors due to their computation complexity with the double weights mechanism. In the present work we investigate the parameter estimates when fitting the quantile regression model in the above modelling approach. We also apply the MI method and evaluate its comparative performance under various missing data scenarios. Re-sults from simulation experiment showed that the MI method was easy to implemented and there was substantial bias in the estimates when fitting the quantile regression model with naive techniques for missing data, under missing at random cause of failure. Compared to those tech-niques, the MI-based method gave estimates with much smaller biases and coverage probabilities of 95 percent confidence intervals closer to the nominal level.The contents of the paper are as follows. In section 1, we introduce the development background of our issue and provide the preliminary. In section 2, model and method are proposed. In section 3, the finite sample performance of our method is investigated. In section 4, real dataset is demonstrated by applying our approach. In section 5, asymptotic properties are established. Section 6 ends the paper with a brief discussion. |
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