From: "INTV (Ivan Nestorov)" nestorvi@zgi.com Subject: [NMusers] Bootstrap question Date: Fri, 22 Sep 2006 15:30:23 -0700 Dear NONMEM, I would like to bootstrap a dataset that includes 4 dosing arms with 15, 20, 15, and 25 subjects in each arm respectively. Do I bootstrap from within each arm so that I have the above sample sizes per arm in each bootstrap file or I just bootstrap the data as a whole and leave the distribution of subjects across the four arms to the random number generators. Thanks. Ivan Nestorov _______________________________________________________ From: Nick Holford n.holford@auckland.ac.nz Subject: Re: [NMusers] Bootstrap question Date: Sat, 23 Sep 2006 10:55:39 +1200 Ivan, If the purpose of the BS is to estimate the uncertainty in the parameter estimates obtained from a particular data set then (e.g. 90% confidence interval [CI]) then the BS should try to retain the original design i.e. you should bootstrap from each arm separately. Note that in theory one should also maintain the same within subject design i.e. the same sampling times. With a non-parametric bootstrap the latter is quite hard to do (if you use actual rather than nominal times) and most people (including me) ignore this requirement. In fact, I would not worry too much about maintaining the original number of subjects per arm. I doubt if it would make much difference which way you did it when it came to interpreting the results. Ignoring the treatment design and doing a non-parametric bootstrap across the whole data set would probably inflate the CI a bit. For typical quantitative applications of the CI (e.g. testing hypotheses for model building) then this bias would tend to make you be a little bit more parsiminous and conservative before accepting new parameters in the model. In my experience it is unusual that anyone would really care much about the bias of a CI for the parameter estimates. Its just something one does to get some rough idea of the uncertainty and to satisfy obsessional journal reviewers/editors. Nick -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/ _______________________________________________________ From: Leonid Gibiansky leonidg@metrumrg.com Subject: Re: [NMusers] Bootstrap question Date: Fri, 22 Sep 2006 19:03:13 -0400 If kinetics is linear, it should not matter. If you observe (or would like to investigate) dose effect, I would keep relative fractions of arm relatively stable, for example by sampling within each group as you suggested Leonid _______________________________________________________ From: Chuanpu.Hu@sanofi-aventis.com Subject: Re: [NMusers] Bootstrap question Date: Tue, 26 Sep 2006 Very well said, Nick. Although when we did this, we really cared. :-) Chuanpu _______________________________________________________ From: Nick Holford n.holford@auckland.ac.nz Subject: Re: [NMusers] Bootstrap question Date: Tue, 26 Sep 2006 08:32:00 +1200 Chuanpu, I'd be interested to know why you really cared about the bias in a bootstrap CI. Its hard to imagine a situation when one might care for the CI on PK parameters but perhaps you were really interested in a PD parameter? Nick -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/ _______________________________________________________ From: Mark Sale - Next Level Solutions mark@nextlevelsolns.com Subject: Re: [NMusers] Bootstrap question Date: Tue, 26 Sep 2006 Nick, I can think of two: 1. PPC - including uncertainty of parameters, although yours and Jogas work suggests it doesn't matter. 2. Bayesian dose adjustment _______________________________________________________ From: Nick Holford n.holford@auckland.ac.nz Subject: Re: [NMusers] Bootstrap question Date: Tue, 26 Sep 2006 10:40:52 +1200 Mark, Your responses are too short for me to understand why bias in a bootstrap CI is important in these cases. Can you explain in each case (with an explicit example) why you think bias would be important and giving an idea of what magnitude of bias you would consider important? It should be kept firmly in mind that CIs obtained with the asymptotic standard error method are likely to be biased because 1) they are asymptotic and 2) the CI calculation nearly always is based on assuming a normal distribution and thus symmetry of the interval. Nick -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/ _______________________________________________________