Marc gave the referencer for Schoenfeld's article. It's actually quite
simple.
Sample size for a Cox model has two parts:
1. Easy part: how many deaths to I need
d = (za + zb)^2 / [var(x) * coef^2]
za = cutoff for your alpah, usually 1.96 (.05 two-sided)
zb = cutoff for
for Cox regression with a time-varying covariate
To: Marc Schwartz marc_schwa...@me.com, Greg Snow 538...@gmail.com,
r-help@r-project.org, Paul Miller pjmiller...@yahoo.com
Received: Wednesday, July 18, 2012, 8:24 AM
Marc gave the referencer for Schoenfeld's article. It's actually quite simple
, Greg Snow 538...@gmail.com* wrote:
From: Greg Snow 538...@gmail.com
Subject: Re: [R] Power analysis for Cox regression with a time-varying
covariate
To: Paul Miller pjmiller...@yahoo.com
Cc: r-help@r-project.org
Received: Friday, July 13, 2012, 3:29 PM
For something like this the best
...@gmail.com* wrote:
From: Greg Snow 538...@gmail.com
Subject: Re: [R] Power analysis for Cox regression with a time-varying
covariate
To: Paul Miller pjmiller...@yahoo.com
Cc: r-help@r-project.org
Received: Friday, July 13, 2012, 3:29 PM
For something like this the best (and possibly only
start out using the steps
you've listed and see where that takes me.
Paul
--- On Fri, 7/13/12, Greg Snow 538...@gmail.com wrote:
From: Greg Snow 538...@gmail.com
Subject: Re: [R] Power analysis for Cox regression with a time-varying covariate
To: Paul Miller pjmiller...@yahoo.com
Cc: r-help
For something like this the best (and possibly only reasonable) option
is to use simulation. I have posted on the general steps for using
simulation for power studies in this list and elsewhere before, but
probably never with coxph.
The general steps still hold, but the complicated part here will
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