SMOOTHED RANK REGRESSION FOR THE ACCELERATED FAILURE TIME COMPETING RISKS MODEL WITH MISSING CAUSE OF FAILURE

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

2 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)23-46
Journal / PublicationStatistica Sinica
Volume29
Issue number1
Publication statusPublished - Jan 2019

Abstract

This paper examines the accelerated failure time competing risks model with missing cause of failure using the monotone class rank-based estimating equations approach. We handle the non-smoothness of the rank-based estimating equations using a kernel smoothed estimation method, and estimate the unknown selection probability and the conditional expectation by non-parametric techniques. Under this setup, we propose three methods for estimating the unknown regression parameters: inverse probability weighting, estimating equations imputation, and augmented inverse probability weighting. We also obtain the associated asymptotic theories of the proposed estimators and investigate their small sample behaviour in a simulation study. A direct plug-in method is suggested for estimating the asymptotic variances of the proposed estimators. A data application based on a HIV vaccine efficacy trial study is considered.

Research Area(s)

  • Accelerated failure time model, competing risks, imputation, inverse probability weighting, missing at random, monotone estimating equation, rank-based estimator, U-statistic, HAZARDS REGRESSION, COEFFICIENTS, ESTIMATORS, INFERENCE, SUBDISTRIBUTION, EXTENSIONS, IMPUTATION, VACCINE, TESTS, STEP