That's the primary reason for the plot: so that you can look and think.
The test statistic is based on whether a LS line fit to the plot has zero slope. For
larger data sets you can sometimes have a "significant" p-value but good agreement with
proportional hazards. It's much like an example from Lincoln Moses' begining statistics
book (now out of print, so rephrasing from memory).
"Suppose that you flip a coin 10,000 times and get 5101 heads. What can you
say?
a. The coin is not perfectly fair (p<.05). b. But it is darn close to
perfect! "
As a referee I would be comfortable using that coin to start a football game.
The Cox model gives an average hazard ratio, averaged over time. When proportional
hazards holds that value is a complete summary-- nothing else is needed. When it does
not hold, the average may still be useful, or not, depending on the degree of change over
time.
Terry Therneau
On 08/13/2013 05:00 AM, r-help-requ...@r-project.org wrote:
Thanks to Bert and G?ran for your responses.
To answer G?ran's comment, yes I did plot the Schoenfeld residuals using
plot.cox.zph and the lines look horizontal (slope = 0) to me, which makes
me think that it contradicts the results of cox.zph.
What alternatives do I have if I assume proportional assumption of coxph
does not hold?
Thanks!
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