From: Jakob Ribbing <*jakob.ribbing*>

Date: Sat, 7 Apr 2018 07:24:06 +0200

Hi Tingjie,

It does not: Sigma squared is the sum of all error variances, and assay =

error in most cases is only a small contribution to this sum.

There are exceptions, but when applying a previous model to new data it =

is rarely the first modification that comes to my mind.

Given your objectives with the model, maybe the best evaluation would be =

to obtain individual parameters based on subject’s first visit =

(IGNORE later time points in $DATA), and then see how well these etas =

predict the DV at subsequent visits?

To split by visit is only an example, obviously, if your project is in =

anesthesia it may be too late for dose adjustment after the visit is =

over, and in other cases observations over several days or weeks may be =

more relevant, for prediction of even later points in time.

Best regards

Jakob

*> On 6 Apr 2018, at 21:40, Tingjie Guo <iam *

*>
*

*> Small correction in question 2: SIGMA (instead of OMEGA) value =
*

influences individual ETAs...

*>
*

*>
*

*> *

*>
*

*> *

to evaluate the predictive ability of the model in particular subjects =

(external data) in order to guide clinical care for these subjects. Does =

this purpose alter your opinion on SIGMA choice?

*>
*

*>
*

*> Yours sincerely,
*

*> Tingjie Guo
*

*>
*

*>
*

*> On Fri, Apr 6, 2018 at 7:51 PM, Leonid Gibiansky =
*

<lgibiansky

*> It would be better to use
*

*>
*

*> $EST METHOD=1 INTERACTION MAXEVAL=0
*

*>
*

*> (at least if the original model was fit with INTERACTION option and =
*

residual error model is not additive).

*>
*

*> One option is to use Para = THETA * EXP(ETA)
*

*> You would be changing the model, but the model is not too good any way =
*

if you need to restrict Para > 0 artificially.

*>
*

*> SIGMA should be taken from the model.
*

*>
*

*> Leonid
*

*>
*

*>
*

*>
*

*> On 4/6/2018 12:32 PM, Tingjie Guo wrote:
*

*> Dear NMusers,
*

*>
*

*> I have two questions regarding the statistical model when performing =
*

external validation. I have a dataset and would like to validate a =

published model with POSTHOC method i.e. $EST METHOD=0 POSTHOC =

MAXEVAL=0.

*>
*

*> 1. The model added etas in proportional way, i.e. Para = THETA * =
*

(1+ETA) and this made the posthoc estimation fail due to the negative =

individual parameter estimate in some subjects. I constrained it to be =

positive by adding ABS function i.e. Para = THETA * ABS(1+ETA), and =

the estimation can be successfully running. I was wondering if there is =

better workaround?

*>
*

*> 2. OMEGA value influences individual ETAs in POSTHOC estimation. =
*

Should we assign $SIGMA with model value or lab (where external data was =

determined) assay error value? If we use model value, it's =

understandable that $SIGMA contains unexplained variability and thus it =

is a part of the model. However, I may also understand it as that model =

value contains the unexplained variability for original data (in which =

the model was created) but not for external data. I'm a little confused =

about it. Can someone help me out?

*>
*

*> I would appreciate any response! Many thanks in advance!
*

*>
*

*> Your sincerely,
*

*>
*

*> Tingjie Guo
*

*>
*

*>
*

Received on Sat Apr 07 2018 - 01:24:06 EDT

Date: Sat, 7 Apr 2018 07:24:06 +0200

Hi Tingjie,

It does not: Sigma squared is the sum of all error variances, and assay =

error in most cases is only a small contribution to this sum.

There are exceptions, but when applying a previous model to new data it =

is rarely the first modification that comes to my mind.

Given your objectives with the model, maybe the best evaluation would be =

to obtain individual parameters based on subject’s first visit =

(IGNORE later time points in $DATA), and then see how well these etas =

predict the DV at subsequent visits?

To split by visit is only an example, obviously, if your project is in =

anesthesia it may be too late for dose adjustment after the visit is =

over, and in other cases observations over several days or weeks may be =

more relevant, for prediction of even later points in time.

Best regards

Jakob

influences individual ETAs...

to evaluate the predictive ability of the model in particular subjects =

(external data) in order to guide clinical care for these subjects. Does =

this purpose alter your opinion on SIGMA choice?

<lgibiansky

residual error model is not additive).

if you need to restrict Para > 0 artificially.

external validation. I have a dataset and would like to validate a =

published model with POSTHOC method i.e. $EST METHOD=0 POSTHOC =

MAXEVAL=0.

(1+ETA) and this made the posthoc estimation fail due to the negative =

individual parameter estimate in some subjects. I constrained it to be =

positive by adding ABS function i.e. Para = THETA * ABS(1+ETA), and =

the estimation can be successfully running. I was wondering if there is =

better workaround?

Should we assign $SIGMA with model value or lab (where external data was =

determined) assay error value? If we use model value, it's =

understandable that $SIGMA contains unexplained variability and thus it =

is a part of the model. However, I may also understand it as that model =

value contains the unexplained variability for original data (in which =

the model was created) but not for external data. I'm a little confused =

about it. Can someone help me out?

Received on Sat Apr 07 2018 - 01:24:06 EDT