From: Leonid Gibiansky <*LGibiansky*>

Date: Tue, 21 Apr 2009 17:43:02 -0400

I do not think that bootstrap mean is a useful statistics. Median of the

bootstrap distribution could/should be compared with the final-model

point estimates. Precision of the parameter estimates can be evaluated

as a 95% confidence interval defined as an interval between 2.5 and 97.5

percentiles of the bootstrap distributions. Another useful application

of the bootstrap parameter estimates is the investigation of the

correlation between those. A scatter-plot matrix of parameters versus

parameters readily reveals existing correlations. If those are very

strong, the model could/should be improved to remove

over-parameterization. Histograms can be useful if you put the

final-model estimate on top of those distributions. It should be

somewhere close to the center of the bootstrap parameter distribution.

If you like some p-value, you can compute the one-sided or two-sided

probability of observing the value as extreme as the final-model

parameter estimates based on the bootstrap distribution (using a percent

of bootstrap estimates that are below or above the final-model. estimate).

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Nick Holford wrote:

*> Varsha,
*

*>
*

*> Congratulations on discovering how to use a bootstrap to evaluate the
*

*> distribution of your model parameter estimates.
*

*>
*

*> The bootstrap mean is probably a more robust estimate of the true value
*

*> of the parameter than the value estimated from the original data. I
*

*> prefer to report the bootstrap mean for this reason.
*

*>
*

*> The uncertainty, e.g. 95% confidence interval, can sometimes be useful
*

*> for model evaluation but more commonly is is best used to keep journal
*

*> reviewers 'happy'. There are very few other real applications of knowing
*

*> the uncertainty of a single parameter but it might be used to try to
*

*> demonstrate that a PD parameter (e.g. Emax) is different from zero and
*

*> thus indicate that the drug does something useful.
*

*>
*

*> The good news is that you don't have to worry about using bootstraps "to
*

*> confirm the fact that the model I have is the best fit for the data".
*

*> The bootstrap can never confirm this for you. You need to buy a
*

*> subscription to 'Talk to God' in order to get that kind of information.
*

*>
*

*> Nick
*

*>
*

*>
*

*> Varsha Mehta wrote:
*

*>> Group:
*

*>>
*

*>> I have bootstrap analysis (my first) parameter estimates and model
*

*>> parameters. The PDxPOP/NONMEM manual I have does not provide
*

*>> any guidance as to how I can statistically compare these two (or do I
*

*>> need to?). I also have histograms for the thetas in bootstrap analysis.
*

*>> I can make some visual judgements but is there a way to statistically
*

*>> compare the two results (bootstrap v model) built in to the NONMEM
*

*>> that I can use to quickly get some statistical comparison results?
*

*>>
*

*>> How else can I use the bootstrap results to confirm the fact that the
*

*>> model I have is the best fit for the data?
*

*>>
*

*>>
*

*>> Thanks in advance.
*

*>>
*

*>> Varsha Mehta, MS(CRDSA), Pharm.D., FCCP
*

*>> Clinical Associate Professor
*

*>> Pharmacy, Pediatrics and Communicable Diseases
*

*>> Clinical Pharmacist Neonatal Critical Care
*

*>> University of Michigan
*

*>> (O) 734-936-8985
*

*>> (F) 734-936-6946
*

*>> varsham *

*>>
*

*>> **********************************************************
*

*>> Electronic Mail is not secure, may not be read every day, and should
*

*>> not be used for urgent or sensitive issues
*

*>>
*

*> *

Received on Tue Apr 21 2009 - 17:43:02 EDT

Date: Tue, 21 Apr 2009 17:43:02 -0400

I do not think that bootstrap mean is a useful statistics. Median of the

bootstrap distribution could/should be compared with the final-model

point estimates. Precision of the parameter estimates can be evaluated

as a 95% confidence interval defined as an interval between 2.5 and 97.5

percentiles of the bootstrap distributions. Another useful application

of the bootstrap parameter estimates is the investigation of the

correlation between those. A scatter-plot matrix of parameters versus

parameters readily reveals existing correlations. If those are very

strong, the model could/should be improved to remove

over-parameterization. Histograms can be useful if you put the

final-model estimate on top of those distributions. It should be

somewhere close to the center of the bootstrap parameter distribution.

If you like some p-value, you can compute the one-sided or two-sided

probability of observing the value as extreme as the final-model

parameter estimates based on the bootstrap distribution (using a percent

of bootstrap estimates that are below or above the final-model. estimate).

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Nick Holford wrote:

Received on Tue Apr 21 2009 - 17:43:02 EDT