Journal ArticleDOI
Analysis of competing risks survival data when some failure types are missing
Els Goetghebeur,Louise Ryan +1 more
TLDR
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.Abstract:
We propose a method to analyse competing risks survival data when failure types are missing for some individuals. Our approach is based on a standard proportional hazards structure for each of the failure types, and involves the solution to estimating equations. We present consistent and asymptotically normal estimators of the regression coefficients and related score tests. An appealing feature is that individuals with known failure types make the same contributions as they would to a standard proportional hazards analysis. Contributions of individuals with unknown failure types are weighted according to the probability that they failed from the cause of interest. Efficiency and robustness are discussed. Results are illustrated with data from a breast cancer trial.read more
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BookDOI
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family
Andrew Gelman,Xiao-Li Meng +1 more
TL;DR: Applied Bayesian modeling and causal inference from incomplete-data perspectives as discussed by the authors, applied Bayesian modelling and causality from incomplete data perspectives, Applied Bayesian model and inference in incomplete data perspective.
Book
Applied Bayesian Modeling And Causal Inference From Incomplete-Data Perspectives
Andrew Gelman,Meng Xiaoli +1 more
TL;DR: Applied Bayesian modeling and causal inference from incomplete-data perspectives, Applied Bayesian modeled and causal inferability from incomplete data perspectives, and more.
Journal ArticleDOI
Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure
Kaifeng Lu,Anastasios A. Tsiatis +1 more
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
Biometrika Centenary: Survival analysis
TL;DR: A survey of the development of survival analysis throughout the twentieth century as reflected in the pages of Biometrika, focusing primarily on work published since 1980, is presented in this paper.
Journal ArticleDOI
Survival with competing risks and masked causes of failures
TL;DR: In this article, the authors show how stage 1 and stage 2 information can be combined to provide statistical inference about (a) survival functions of individual risks, (b) the proportions of failures associated with individual risks and (c) probability, for a specified masked case, that each of the masked competing risks is responsible for the failure.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI
Statistical Analysis with Missing Data
Book
Analysis of Survival Data
David Cox,D. Oakes +1 more
TL;DR: In this article, the authors give a concise account of the analysis of survival data, focusing on new theory on the relationship between survival factors and identified explanatory variables and conclude with bibliographic notes and further results that can be used for student exercises.
Related Papers (5)
Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure
Kaifeng Lu,Anastasios A. Tsiatis +1 more
A modified log rank test for competing risks with missing failure type
Els Goetghebeur,Louise Ryan +1 more