Jim, I respectfully disagree, and there is 5 decades of literature to back me up. Berkson and Gage (1950) is in response to medical papers that summarized surgical outcomes using only the observed deaths, and shows important failings of the method. Ignoring the censored cases usually gives biased answers, often so badly so that they are misleading and worse than no answer at all. The PH model is surprisingly accurate in acute disease (I work in areas like multiple myeloma and liver transplant so see a lot of this) and is also used in economics (duration of unemployment for instance), the accelerated failure time models have proven very reliable predictors in industry work. Censored linear regression (e.g. "Tobit" model) is not uncommon. I am not aware of any cases where ignoring the censored cases gives a competitive answer. Blindly using a coxph model without checking into or at least thinking about the proportional hazards assumption is dangerous, but so is blind use of any other model.
Terry T. ------- Begin included message ------------- Terry, My point was that if you are asking the question: What is the average time to death based on a set of variables? The only logical approach for calculating actual time to death is to use uncensored cases, because we do not know the time to death for the censored cases and can only estimate them. While actual time to death for uncensored cases may not be a very useful piece of information, it can indeed be calculated. However, as you point out predicted values for time to death can be estimated using the survival function which incorporates both censored and uncensored data. However, the assumption of proportional hazards is rarely defensible. Best, Jim ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
