scispace - formally typeset
Open AccessJournal ArticleDOI

Regression analysis of competing risks data with general missing pattern in failure types

TLDR
In this paper, the cause-specific hazard rates under the general missing pattern were estimated using some semi-parametric models, and the regression coefficients and the baseline hazards were investigated.
About
This article is published in Statistical Methodology.The article was published on 2016-03-01 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Missing data & Nelson–Aalen estimator.

read more

Citations
More filters
Book ChapterDOI

On Competing Risks with Masked Failures

TL;DR: In this article, some statistical inference procedures used when the cause of failure is missing or masked for some units are reviewed.
Journal ArticleDOI

Nonparametric Estimation of Cumulative cause Specific Reversed Hazard Rates under Masked Causes of Failure

TL;DR: This paper considers the nonparametric estimation of cumulative cause specific reversed hazard rates for left censored competing risks data under masked causes of failure with maximum likelihood estimators and least squares type estimators.
References
More filters
Journal ArticleDOI

Asymptotic Distribution Theory for Cox-Type Regression Models with General Relative Risk Form

TL;DR: In this article, the authors use the counting process formulation of Andersen and Gill (1982) to develop asymptotic distribution theory for a class of intensity function regression models in which the usual exponential regression form is relaxed to an arbitrary non-negative twice differentiable form.
Journal ArticleDOI

Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure

TL;DR: A method to estimate the regression coefficients in a competing risks model where the cause-specific hazard for the cause of interest is related to covariates through a proportional hazards relationship and when cause of failure is missing for some individuals is proposed.
Journal ArticleDOI

Nonparametric estimation for partially-complete time and type of failure data.

Gregg E. Dinse
- 01 Jun 1982 - 
TL;DR: This paper considers partially-complete outcomes, for which only one of the random variables if fully observed is observed, and an iterative algorithm yields distribution-free estimates of the joint law governing this random pair; these estimates converge to the maximum likelihood solution.
Journal ArticleDOI

Analysis of competing risks survival data when some failure types are missing

TL;DR: In this paper, the authors proposed a method to analyse competing risks survival data when failure types are missing for some individuals, based on a standard proportional hazards structure for each of the failure types, and involves the solution to estimating equations.
Related Papers (5)
Frequently Asked Questions (1)
Q1. What have the authors contributed in "Regression analysis of competing risks data with general missing pattern in failure types" ?

In this work, the authors deal with the regression problem, in which the cause-specific hazard rates may depend on some covariates, and consider estimation of the regression coefficients and the cause-specific baseline hazards under the general missing pattern using some semi-parametric models. The authors consider two different proportional hazards type semi-parametric models for their analysis. The authors also consider an example from an animal experiment to illustrate their methodology.