y**3
发帖数: 267 | 1 For model methods used for repeated events such as counting process,
marginal model,is it theroretically correct to look into future? If so, can
the current available software such as sas R output individual risk
estimates at a specififc time especially at a time beyond the observed time
for each individual pateint level data? Or output the survival time for
individual patient for the event?
Any expert can provide expereince?
Thanks |
A*******s
发帖数: 3942 | 2 depends...
for AG models with time dependent variable, you have to also forecast the
future paths of these independent variables.
For frailty models, could plug in the empirical Bayes estimate of the
frailty term into the prediction.
And, more meaningful prediction is on the density rather than survival time.
can
time
【在 y**3 的大作中提到】
: For model methods used for repeated events such as counting process,
: marginal model,is it theroretically correct to look into future? If so, can
: the current available software such as sas R output individual risk
: estimates at a specififc time especially at a time beyond the observed time
: for each individual pateint level data? Or output the survival time for
: individual patient for the event?
: Any expert can provide expereince?
: Thanks
|
y**3
发帖数: 267 | 3 Thanks for the answer. It is very helpful. Will dig mroe aobut those two
models.
Can you elaborate a little for me?
So the models can be used to generate future predictions such as hazard
probabilities for the next month from date of censoring for scoring? Then
what assumption is used? Have you ever to put repeated event survival model
into production tp monitor fututre event?
Btw,I read somewhere the counting process is too easy to look into future.IS
that correct:) |
C******n
发帖数: 284 | 4 As far as I understand, you have to estimate a parametric model to predict
future survival time. |
y**3
发帖数: 267 | 5 Thanks! I got the point! I found out that some frailty models such as in R
package Frailtypack use parametric method to projection into future. not
sure sas yet |
y**3
发帖数: 267 | 6 thanks! Danou!
I did some research on repeated events survial model. As you said, a shared
frailty model fits my purpose well.
I have a question for what you said-For frailty models, could plug in the
empirical Bayes estimate of the prediction. Does this mean for time varying(
dependnet) covariates, that I dont need forecast their future path when
scoring to the near future?
thanks again
time. |
A*******s
发帖数: 3942 | 7 frailty and time dependent are not mutually exclusive. You could include
both in one model. As long as there is time dependent variable, you need to
forecast its path. Keep in mind the time dependent variable has to be
external and predictable.
shared
varying(
【在 y**3 的大作中提到】
: thanks! Danou!
: I did some research on repeated events survial model. As you said, a shared
: frailty model fits my purpose well.
: I have a question for what you said-For frailty models, could plug in the
: empirical Bayes estimate of the prediction. Does this mean for time varying(
: dependnet) covariates, that I dont need forecast their future path when
: scoring to the near future?
: thanks again
: time.
|
y**3
发帖数: 267 | 8
to
【在 A*******s 的大作中提到】
: frailty and time dependent are not mutually exclusive. You could include
: both in one model. As long as there is time dependent variable, you need to
: forecast its path. Keep in mind the time dependent variable has to be
: external and predictable.
:
: shared
: varying(
|
y**3
发帖数: 267 | 9 Thanks, Danou!
I was not able to find concrete methods to forecast time dependent and time
varing covariates, especially for a large of observations. For time varying
covariates such as blood pressure, number of complaints, can I use lag, or
constant, or average to plug into the future forecast ? what is the common
practice in industries?
Thanks so much! Appreciatate it
to
【在 A*******s 的大作中提到】
: frailty and time dependent are not mutually exclusive. You could include
: both in one model. As long as there is time dependent variable, you need to
: forecast its path. Keep in mind the time dependent variable has to be
: external and predictable.
:
: shared
: varying(
|
