Dear all
I would be most grateful to receive views on the usage of Cox regression in a piece of work I read recently. The research involved comparing different staging systems for cancer with respect to their capacity to stratify patients according to the risk of mortality. Part of this work involved constructing a multiple covariate model in which the different staging systems were factors and the stages were the levels of these factors. The authors uses Wald's test and the extent to which the -Log-likelihood statistic was changed on removing any one factor from this model to compare the systems. No mention is made of the fact that Cox regression adjusts for confounding. However, I am aware from my own work that, due to confounding, the best system for stratification obtained from individual log-rank tests (in which stages are compared for any given system within the context of a Kaplan-Meier analysis) need not be the same as the system which makes the highest independent cont!
ribution
to a proportional hazards model.
The only thing I can see that the authors could have gained from the multiple covariate Cox regression approach is the ability to build a model which combines different staging systems, so as to obtain a hybrid but more successful staging system. However, they do not do this. They simply state the results of the Cox regression analysis. What troubles me about this is that if the purpose of the exercise is simply to compare the staging systems as stand alone systems, why should they taking confounding into consideration? What does this tell the reader when deciding which is the best system to adopt when predicting a patient's chance of survival? Why would they wish to know the INDEPENDENT stratification value of each system?
Perhaps I am missing something. I would therefore very much value some feedback, as it would appear that the associated paper was not written by statisticians.
Thank you very much
Regards
Margaret
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