Asymptotic efficiency of a competing risks model

A Two-Sample case

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A generalized Cox regression model is studied for the covariance analysis of competing risks data subject to independent random censoring. The information of the maximum partial likelihood estimates is compared with that of maximum likelihood estimates assuming a log linear hazard function. The method of generalized variance is used to define the efficiency of estimation between the two models. This is then applied to two-sample problems with two exponentially censoring rates. Numerical results are summarized and presented graphically. The detailed results indicate that the semi-parametric model works well for a higher rate of censoring. A method of generalizing the result to type 1 censoring and the efficiency of estimating the coefficient of the covariate are discussed. A brief account of using the results to help design experiments is also given.

Original languageEnglish (US)
Pages (from-to)3159-3176
Number of pages18
JournalCommunications in Statistics - Theory and Methods
Volume22
Issue number11
DOIs
StatePublished - Jan 1 1993
Externally publishedYes

Fingerprint

Competing Risks Model
Asymptotic Efficiency
Censoring
Cox Regression Model
Generalized Variance
Random Censoring
Two-sample Problem
Analysis of Covariance
Partial Likelihood
Competing Risks
Hazard Function
Semiparametric Model
Maximum Likelihood Estimate
Linear Function
Maximum Likelihood
Covariates
Numerical Results
Coefficient
Estimate
Experiment

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

Asymptotic efficiency of a competing risks model : A Two-Sample case. / Wan, Jim.

In: Communications in Statistics - Theory and Methods, Vol. 22, No. 11, 01.01.1993, p. 3159-3176.

Research output: Contribution to journalArticle

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