Dear R Fellow-Travellers: What is your recommended way of dealing with a left-censored response (non-detects) in (linear Gaussian) mixed effects models? Specifics: Response is a numeric positive measurement (of volume, actually); but when it falls below some unknown and slightly random value (depending on how the sample is prepared and measured), it cannot be measured and is recorded as 0. There is some statistical literature on this, but I was unable to find anything that appeared to me to implement a strategy in any R package. If it matters, I am less interested in inference than in removing possible bias in estimation. Feel free to respond off-list if you feel that this would not be of general interest. Cheers, Bert Gunter Genentech
I assume you've looked at the NADA package(?) While I don't believe it goes as far as dealing the mixed effects models, it might give you a starting point, and possibly some additional references. -Don At 9:08 AM -0700 5/12/08, Bert Gunter wrote:>Dear R Fellow-Travellers: > >What is your recommended way of dealing with a left-censored response >(non-detects) in (linear Gaussian) mixed effects models? > >Specifics: Response is a numeric positive measurement (of volume, actually); >but when it falls below some unknown and slightly random value (depending on >how the sample is prepared and measured), it cannot be measured and is >recorded as 0. > >There is some statistical literature on this, but I was unable to find >anything that appeared to me to implement a strategy in any R package. If it >matters, I am less interested in inference than in removing possible bias in >estimation. > >Feel free to respond off-list if you feel that this would not be of general >interest. > >Cheers, > >Bert Gunter >Genentech > >______________________________________________ >R-help at r-project.org mailing list >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code.-- -------------------------------------- Don MacQueen Environmental Protection Department Lawrence Livermore National Laboratory Livermore, CA, USA 925-423-1062
Bert Gunter <gunter.berton <at> gene.com> writes:> Dear R Fellow-Travellers: > > What is your recommended way of dealing with a left-censored response > (non-detects) in (linear Gaussian) mixed effects models?Your description of the data calls for a tobit model http://en.wikipedia.org/wiki/Tobit_model I think you need to take a look in the survival package. Regards, Gregor
Giovanni Parrinello
2008-May-13 07:45 UTC
[R] Left censored responses in mixed effects models(II)
Dear Bert, you can also translate in nlme, as I'm trying to do, the approach of Thi?baut and *Gadda( *Mixed models for longitudinal left-censored repeated measures. Computer Methods and Programs in Biomedicine 74 <http://www.informatik.uni-trier.de/%7Eley/db/journals/cmpb/cmpb74.html#ThiebautJ04>(3): <javascript:void(0)>(2004)) written in nlmixed(SAS) Giovanni Bert Gunter ha scritto:> Dear R Fellow-Travellers: > > What is your recommended way of dealing with a left-censored response > (non-detects) in (linear Gaussian) mixed effects models? > > Specifics: Response is a numeric positive measurement (of volume, actually); > but when it falls below some unknown and slightly random value (depending on > how the sample is prepared and measured), it cannot be measured and is > recorded as 0. > > There is some statistical literature on this, but I was unable to find > anything that appeared to me to implement a strategy in any R package. If it > matters, I am less interested in inference than in removing possible bias in > estimation. > > Feel free to respond off-list if you feel that this would not be of general > interest. > > Cheers, > > Bert Gunter > Genentech > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > >-- dr. Giovanni Parrinello Department of Biotecnologies Medical Statistics Unit University of Brescia Viale Europa, 11 25123 Brescia email: parrinel at med.unibs.it Phone: +390303717528 Fax: +390303717488
Bert Gunter wrote:> > What is your recommended way of dealing with a left-censored response > (non-detects) in (linear Gaussian) mixed effects models? > > Specifics: Response is a numeric positive measurement (of volume, > actually); > but when it falls below some unknown and slightly random value (depending > on > how the sample is prepared and measured), it cannot be measured and is > recorded as 0. > > There is some statistical literature on this, but I was unable to find > anything that appeared to me to implement a strategy in any R package. If > it > matters, I am less interested in inference than in removing possible bias > in > estimation. >The simplest way is to substitute some number for the nondetects, typically half the limit of detection. This method is usually fine if you have less than about 10% nondetects, but can lead to big biases with larger numbers of nondetects. Denis Helsel has written fairly extensively on this topic, and is the author of the book "Nondetects and data analysis", which the NADA R package is based upon. He discusses it here: http://www.practicalstats.com/nada/nadafiles/files/NADAforR_Examples.pdf http://www.practicalstats.com/nada/nadafiles/files/NADAforR_Examples.pdf Possibly the best option is to use OpenBUGS to run an MCMC model. It has a nice interface with R using the R2WinBUGS package. Be warned though, this option does require some understanding of Bayesian stats. ----- Regards, Richie. Mathematical Sciences Unit HSL -- View this message in context: http://www.nabble.com/Left-censored-responses-in-mixed-effects-models-tp17190869p17205469.html Sent from the R help mailing list archive at Nabble.com.