Dr. Therneau,
Thank you as always for first writing, and second continuing the Cox model in R
(and earlier I believe in SAS).
While your comments concerning non-proportional hazards is helpful, it does not
fully address the question, What alternatives do I have if I assume
proportional
be close enough to be useful.
This is always a problem with goodness-of-fit tests and large datasets.
Chris
-Original Message-
From: Soumitro Dey [mailto:soumitrod...@gmail.com]
Sent: Tuesday, August 13, 2013 10:38 AM
To: Terry Therneau
Cc: r-help@r-project.org
Subject: Re: [R] coxph
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
Thank you for your response, Terry.
To put the discussion into perspective, my data set is quite large with
over 160,000 samples and 38 variables. The event is true for all samples in
this dataset. The distribution is zero-inflated (i.e. most events occur at
time = 0).
The result of the cox.zph
Thanks to Bert and Göran for your responses.
To answer Göran'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
On 08/11/2013 06:14 AM, Soumitro Dey wrote:
Hello all,
This may be a naive question but since I'm new to R/survival models, I
cannot figure it out the problem myself.
I have a coxph model for my data and I am trying to test if the
proportional hazards assumption holds. Using cox.zph on the
Hello all,
This may be a naive question but since I'm new to R/survival models, I
cannot figure it out the problem myself.
I have a coxph model for my data and I am trying to test if the
proportional hazards assumption holds. Using cox.zph on the model I get a
global score:
GLOBAL NA 4.20e+02
Although someone on this list may respond, AFAICS this does not seem
to be an R question for R-help.I would suggest that you spend some
time with a local statistician.
A general observation: Statistical model assumptions neither :hold
nor don't hold. Quoting George Box, All models are wrong, but
Similarly, when I do plot(zph), B(t) is fairly non-constant.
This isn't inherently a problem for me. I don't need a hard single number
to characterize the shape of the excess risk. However, I'd like to be
able to say
something qualitative about the shape of the excess risk for the predictor.
Hi,
I've been banging my head against the following problem for a while
and thought the fine people on r-help might be able to help. I'm
using the survival package.
I'm studying the survival rate of a population with a preexisting
linear-like event rate (there are theoretical reasons to believe
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