To elaborate on Frank's response, the analysis plan of
1. Look at the data and select important variables
2. Put that truncated list into your favorite statistic procedure
3. Ask - are the p-values (c-statistic, coefficients, .) reliable?
is a very old plan. The answer to the last
On Sep 29, 2013, at 2:16 PM, E Joffe wrote:
HI,
Thank you for your answer.
There were 301 events out of 394 observations.
Study goals: Identify proteins with prognostic power in patients with AML.
There were 232 proteins studied.
Traditional models won't converge.
I wanted to do a
HI,
Thank you for your answer.
There were 301 events out of 394 observations.
Study goals: Identify proteins with prognostic power in patients with AML.
There were 232 proteins studied.
Traditional models won't converge.
I wanted to do a multivariate survival analysis that would allow me to
Hi all,
I am using COX LASSO (glmnet / coxnet) regression to analyze a dataset of
394 obs. / 268 vars.
I use the following procedure:
1. Construct a coxnet on the entire dataset (by cv.glmnet)
2. Pick the significant features by selecting the non-zero coefficient
under the best lambda
This appears to be a statistics, not an R-help question, so should
probably be asked on a statistics list, not here (e.g.
stats.stackexchange.com).
But if I understand your issue correctly, perhaps the heart f the
matter is: why do you think a stable fit must explain a lot of the
variation? Feel
On Sep 28, 2013, at 2:39 AM, E Joffe wrote:
Hi all,
I am using COX LASSO (glmnet / coxnet) regression to analyze a
dataset of
394 obs. / 268 vars.
I use the following procedure:
1. Construct a coxnet on the entire dataset (by cv.glmnet)
2. Pick the significant features by
This entire procedure is not valid. You cannot use a penalized method for
selecting variables then use an unpenalized procedure on those selected.
Frank
David Winsemius wrote
On Sep 28, 2013, at 2:39 AM, E Joffe wrote:
Hi all,
I am using COX LASSO (glmnet / coxnet) regression to analyze a
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