ArticleStatistica Sinica2019
Smoothed Rank Regression for the Accelerated Failure Time Competing Risks Model with Missing Cause of Failure.
Article in Statistica Sinica, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Modeling Disease-specific Survival in Observational Studies with Missing Cause of Death: Leveraging Information from Clinical Trial Data.Computational statistics & data analysis · 2026Article
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- Framework for constructing an optimal weighted score based on agreement.Journal of applied statistics · 2025Article
- A Smooth Nonparametric Approach to Determining Cut-Points of A Continuous Scale.Computational statistics & data analysis · 2019Article
- Generalized accelerated recurrence time model for multivariate recurrent event data with missing event type.Biometrics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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 based on 1) inverse probability weighting, 2) estimating equations imputation and 3) augmented inverse probability weighting. We also obtain the associated asymptotic theories of the proposed estimators and investigate the estimators' small sample behaviour in a simulation study. A direct plug-in method is suggested for estimating the asymptotic variances of the proposed estimators. A real data application based on a HIV vaccine efficacy trial study is considered.
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