Estimation of surgeon effects in the analysis of post-operative colorectal cancer patients data

Yau, K. K. W., Ng, S. K., Cheung, M. T. and Tung, M. C. (1999) Estimation of surgeon effects in the analysis of post-operative colorectal cancer patients data. Journal of Applied Statistics, 26 2: 257-272. doi:10.1080/02664769922593


Author Yau, K. K. W.
Ng, S. K.
Cheung, M. T.
Tung, M. C.
Title Estimation of surgeon effects in the analysis of post-operative colorectal cancer patients data
Journal name Journal of Applied Statistics   Check publisher's open access policy
ISSN 0266-4763
Publication date 1999-02
Sub-type Article (original research)
DOI 10.1080/02664769922593
Volume 26
Issue 2
Start page 257
End page 272
Total pages 16
Editor G.K. Kanji
Place of publication Basingstoke, UK
Publisher Carfax Publishing Ltd
Collection year 1999
Language eng
Subject C1
730108 Cancer and related disorders
230204 Applied Statistics
Abstract There has been increasing interest in the assessment of surgeon effects for survival data of post-operative cancer patients. In particular, the measurement of surgeon's surgical performance after eliminating significant risk variables is considered The generalized linear mixed model approach, which assumes a log-normal-distributed surgeon effects in the hazard function, is adopted to assess the random surgeon effects of post-operative colorectal cancer patients data. The method extends the traditional Cox's proportional hazards regression model, by including a random component in the linear predictor. Estimation is accomplished by constructing an appropriate log-likelihood function in the spirit of the best linear unbiased predictor method and extends to obtain residual maximum-likelihood estimates. As a result of the non-proportionality of the hazard of colon and rectal cancer, the data are analyzed separately according to these two kinds of cancer. Significant risk variables are identified. The 'predictions' of random surgeon effects are obtained and their association with the rank of surgeon is examined.
Keyword Statistics & Probability
Linear Mixed Models
Clustered Survival-data
Reml Estimation
Mortality
Colon
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Tue, 10 Jun 2008, 13:57:03 EST