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 f...
Complex diseases like cancers can often be classified into subtypes using various pathological and m...
In medical studies or in reliability analysis an investigator is often interested in the assessment ...
While nonparametric methods have been well established for inference on competing risks data, parame...
We propose a method to analyse competing risks survival data when failure types are missing for some...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
Standard survival analysis focuses on failure-time data that has one type of failure. Competing risk...
Goetghebeur and Ryan proposed a method for proportional hazards analyses of competing risks failure-...
Abstract: Competing risks data arise when study subjects may experience several different types of f...
Summary. In competing risks data, missing failure types (causes) is a very common phenomenon. In a g...
Competing risks data usually arises in studies in which the failure of an individual may be classifi...
Competing risks data usually arises in studies in which the failure of an individual may be classifi...
If something can fail, it can often fail in one of several ways and sometimes in more than one way a...
Survival analysis is a powerful statistical tool to study failure-time data. In introductory courses...
We propose vertical modelling as a natural approach to the problem of analysis of competing risks da...
Competing risk or "multiple cause" survival data arise in medical, criminological, financial, engine...
Complex diseases like cancers can often be classified into subtypes using various pathological and m...
In medical studies or in reliability analysis an investigator is often interested in the assessment ...
While nonparametric methods have been well established for inference on competing risks data, parame...
We propose a method to analyse competing risks survival data when failure types are missing for some...
In the competing risks model, a unit is exposed to several risks at the same time, but it is assumed...
Standard survival analysis focuses on failure-time data that has one type of failure. Competing risk...
Goetghebeur and Ryan proposed a method for proportional hazards analyses of competing risks failure-...
Abstract: Competing risks data arise when study subjects may experience several different types of f...
Summary. In competing risks data, missing failure types (causes) is a very common phenomenon. In a g...
Competing risks data usually arises in studies in which the failure of an individual may be classifi...
Competing risks data usually arises in studies in which the failure of an individual may be classifi...
If something can fail, it can often fail in one of several ways and sometimes in more than one way a...
Survival analysis is a powerful statistical tool to study failure-time data. In introductory courses...
We propose vertical modelling as a natural approach to the problem of analysis of competing risks da...
Competing risk or "multiple cause" survival data arise in medical, criminological, financial, engine...
Complex diseases like cancers can often be classified into subtypes using various pathological and m...
In medical studies or in reliability analysis an investigator is often interested in the assessment ...
While nonparametric methods have been well established for inference on competing risks data, parame...