Hi, Ron & Nick: Thank you very much for your helpful suggestions! I will look into those references.
Regards, Siwei On Fri, Oct 3, 2014 at 12:27 AM, Nick Holford <n.holf...@auckland.ac.nz> wrote: > Siwei, > > I agree with Ron. Using the measurements you have is better than trying to > use a work around such as likelihood or imputation based methods. Some > negative measurement values are exactly what you should expect if the true > concentration is zero (or 'close' to zero) when there is background > measurement error noise. > > As far as I know all common methods of measurement (HPLC, MS) have > background noise. You can account for this noise when you model your data > by including an additive term in the residual error model. The additive > error estimate will also include other sources of residual error that are > independent of concentration eg. due to model misspecification. > > Here is a reference to a publication which used measured concentrations > that included negative measured values. Note that a negative measured value > does not mean the actual concentration was negative! > > Patel K, Choy SS, Hicks KO, Melink TJ, Holford NH, Wilson WR. A combined > pharmacokinetic model for the hypoxia-targeted prodrug PR-104A in humans, > dogs, rats and mice predicts species differences in clearance and toxicity. > Cancer Chemother Pharmacol. 2011;67(5):1145-55. > > Best wishes, > > Nick > > On 3/10/2014 11:07 a.m., Ron Keizer wrote: > > hi Siwei, > you should include the BLOQ data as they are, i.e. negative. Any other > approach would decrease precision (e.g. M3 likelihood-based) and/or induce > bias (e.g. LLOQ/2 or LLOQ=0). I've done some simulations on this a while > ago to show this ( > http://page-meeting.org/pdf_assets/2413-PAGE_2010_poster_LLOQ_v1.pdf), > but it should be intuitive too. > best regards, > Ron > > ---------------------------------------------- > Ron Keizer, PharmD PhD > Dept. of Bioengineering & Therapeutic Sciences > University of California San Francisco (UCSF) > ---------------------------------------------- > > On Thu, Oct 2, 2014 at 2:10 PM, siwei Dai <ellen.siwei...@gmail.com> > wrote: > >> Dear NM users: >> >> I have a dataset where some of the concentrations are reported as >> negative values. I believe that the concentrations were calculated using a >> standard curve. >> >> My instinct is to impute all the negative values to zero, but worry >> that it will introduce bias. >> >> A 2nd thought is using the absolute value of the lowest (negative) >> concentration as LLOQ. All the concentrations below LLOQ will be treated as >> zero. By doing this, some positive and negative values both will be zero >> out which will help to cancel some of the unevenness that the 1st method >> may introduce. >> >> I believe that the 2nd method is better but wonder if there is any >> other better way to do it. Any comments will be greatly appreciated. >> >> Thank you in advance. >> >> Siwei >> > > > -- > Nick Holford, Professor Clinical Pharmacology > Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A > University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand > office:+64(9)923-6730 mobile:NZ +64(21)46 23 53 > email: n.holf...@auckland.ac.nzhttp://holford.fmhs.auckland.ac.nz/ > > Holford SD, Allegaert K, Anderson BJ, Kukanich B, Sousa AB, Steinman A, > Pypendop, B., Mehvar, R., Giorgi, M., Holford,N.H.G. Parent-metabolite > pharmacokinetic models - tests of assumptions and predictions. Journal of > Pharmacology & Clinical Toxicology. 2014;2(2):1023-34. > > Ribba B, Holford N, Magni P, Trocóniz I, Gueorguieva I, Girard P, Sarr,C., > Elishmereni,M., Kloft,C., Friberg,L. A review of mixed-effects models of > tumor growth and effects of anticancer drug treatment for population > analysis. CPT: pharmacometrics & systems pharmacology. 2014;Accepted > 15-Mar-2014. > >
