[R] predict.coxph

[Mike Marchywka]

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> Date: Fri, 12 Nov 2010 16:08:57 -0600
> From: therneau at mayo.edu
> To: james.whanger at gmail.com
> CC: r-help at r-project.org; haenlein at escpeurope.eu
> Subject: Re: [R] predict.coxph
>
> 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

( yes I read all the way through and noted your caveats below
but curious about the reality of what you encounter and what would
make sense to consider in the future as better understanding of causality
can remove random events.  )

If you are looking at radioactive decay maybe but how often do
you actually see exponential KM curves in real life? Certainly
depending on MOA of drug or disease/enrollment critera, you could expect qualitative
changes in disease trajectory and consequently in survival
curves.  A  trial design
could in fact try to get all the control sample to "event"  at the same
time if enough was known about prognostic factors and natural trajectory
as this should make drug effects quite clear- a step function of course
is not a constant hazard.( now writing a label based on this trial
may annoy the FDA [ " indicated for patients with exactly 6 months of life expectancy
based on XYZ paper " LOL ] but from a statistical standpoint would seem like
a good idea to consider to get power with few patients). At minimum, there could be
some inital plateau as almost-dead patients may be excluded etc.




> acute disease (I work in areas like multiple myeloma and liver

On the R-related topic, do you know anything about results
with VLA-4 inhibitors in MM?

> 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.

Are you talking about right censored? These points would seem to be
informative as they have survived this long nand simply ignoring them would
create bias. Ceratinly lost to follow
up should be unbiased if just ignored no? Personally I think I finally decided that
comparing integral measures may be more helpful- patient-months of excess
survival for example- rather than asking about things like means or
medians.

So basically your conversation is about calculating things like average 
survival time with many data points yet to event? 

> 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.

As noted above, I wasn't trying to take your earlier statement out of context...

>
> 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
>
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