EconPapers    
Economics at your fingertips  
 

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

K. K. W. Yau

Journal of Applied Statistics, 1999, vol. 26, issue 2, 257-272

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.

Date: 1999
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664769922593 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:26:y:1999:i:2:p:257-272

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664769922593

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:257-272