Re: [R] fitting mixed models to censored data?
Douglas: AFAIK, this is subject area of active current research. Diggle, Heagerty, Liang, and Zeger , 2002, (ANALYSIS OF LONGITUDINAL DATA) say on p.316: An emerging consensus is that analysis of data with potentially informative dropouts necessarily involves assumptions which are difficult, or even impossible, to check from the observed data. This was ca 1994, I believe, so I don't know whether this view is still held among experts (which I am not). But if it is, you may do well to be careful of whatever SAS does even if you do have to go running off to it. Cheers, Bert Gunter Genentech Nonclinical Statistics -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Douglas Grove Sent: Monday, April 23, 2007 10:58 AM To: r-help@stat.math.ethz.ch Subject: [R] fitting mixed models to censored data? Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that SAS's proc nlmixed can accomodate censoring (though proc mixed apparently can't), so if I can't find something available in R, I'll have to break down and use that. Please, save me from having to use SAS! Thanks much, Doug __ R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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.
Re: [R] fitting mixed models to censored data?
Hi Bert, Yes, I am always wary when one software offers something that other do not. The censoring I'm faced with (at present) isn't as complicated as with much 'survival' data. I'm trying to analyze assay data and have a lower limit of detection (LLD) to contend with. Once the level of the analyte gets low enough it can't be accurately quantitated, hence all that is reported is that the level is less than some value (the LLD). So I'm not worried about all the complex assumptions that go along with censoring in clinical trials, etc. Thanks, Doug On Mon, 23 Apr 2007, Bert Gunter wrote: Douglas: AFAIK, this is subject area of active current research. Diggle, Heagerty, Liang, and Zeger , 2002, (ANALYSIS OF LONGITUDINAL DATA) say on p.316: An emerging consensus is that analysis of data with potentially informative dropouts necessarily involves assumptions which are difficult, or even impossible, to check from the observed data. This was ca 1994, I believe, so I don't know whether this view is still held among experts (which I am not). But if it is, you may do well to be careful of whatever SAS does even if you do have to go running off to it. Cheers, Bert Gunter Genentech Nonclinical Statistics -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Douglas Grove Sent: Monday, April 23, 2007 10:58 AM To: r-help@stat.math.ethz.ch Subject: [R] fitting mixed models to censored data? Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that SAS's proc nlmixed can accomodate censoring (though proc mixed apparently can't), so if I can't find something available in R, I'll have to break down and use that. Please, save me from having to use SAS! Thanks much, Doug __ R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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.
Re: [R] fitting mixed models to censored data?
Doug, In perhaps similar situations where there are clusters of measurements due to repeated time or space on an individual subject or experimental unit, I have used the survreg() function from the survival library. You can specify left, right, and/or interval censoring within a data set through Surv(), and so I have used left censoring for the LOD observations. I was just focused on marginal or population-averaged estimation, so the use of cluster() in the argument for survreg() and the robust option in survreg() to get sandwich error estimates was sufficient for me. Depending on your needs to evaluate random effects, frailty() in the survival package -- which can be used with survreg() or coxph() --- is another alternative to explore, I believe. Hope that helps, Bill Nonclinical Statistics, Centocor R D -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Douglas Grove Sent: Monday, April 23, 2007 2:29 PM To: Bert Gunter Cc: r-help@stat.math.ethz.ch Subject: Re: [R] fitting mixed models to censored data? Hi Bert, Yes, I am always wary when one software offers something that other do not. The censoring I'm faced with (at present) isn't as complicated as with much 'survival' data. I'm trying to analyze assay data and have a lower limit of detection (LLD) to contend with. Once the level of the analyte gets low enough it can't be accurately quantitated, hence all that is reported is that the level is less than some value (the LLD). So I'm not worried about all the complex assumptions that go along with censoring in clinical trials, etc. Thanks, Doug On Mon, 23 Apr 2007, Bert Gunter wrote: Douglas: AFAIK, this is subject area of active current research. Diggle, Heagerty, Liang, and Zeger , 2002, (ANALYSIS OF LONGITUDINAL DATA) say on p.316: An emerging consensus is that analysis of data with potentially informative dropouts necessarily involves assumptions which are difficult, or even impossible, to check from the observed data. This was ca 1994, I believe, so I don't know whether this view is still held among experts (which I am not). But if it is, you may do well to be careful of whatever SAS does even if you do have to go running off to it. Cheers, Bert Gunter Genentech Nonclinical Statistics -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Douglas Grove Sent: Monday, April 23, 2007 10:58 AM To: r-help@stat.math.ethz.ch Subject: [R] fitting mixed models to censored data? Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that SAS's proc nlmixed can accomodate censoring (though proc mixed apparently can't), so if I can't find something available in R, I'll have to break down and use that. Please, save me from having to use SAS! Thanks much, Doug __ R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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. [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch 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.
Re: [R] fitting mixed models to censored data?
Hi Douglas, I wonder if frailty models are what you're looking for? https://stat.ethz.ch/pipermail/r-help/2004-November/061511.html Cheers, Andrew On Mon, Apr 23, 2007 at 10:58:17AM -0700, Douglas Grove wrote: Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that SAS's proc nlmixed can accomodate censoring (though proc mixed apparently can't), so if I can't find something available in R, I'll have to break down and use that. Please, save me from having to use SAS! Thanks much, Doug __ R-help@stat.math.ethz.ch 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. -- Andrew Robinson Department of Mathematics and StatisticsTel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 http://www.ms.unimelb.edu.au/~andrewpr http://blogs.mbs.edu/fishing-in-the-bay/ __ R-help@stat.math.ethz.ch 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.
Re: [R] fitting mixed models to censored data?
Hi Bill, Thanks for your reply. The first place I looked was in the survival package since it can obviously handle censored data. However, I don't have any particular desire to restrict myself to standard survival models just because I have some censoring. Frailties appear to fit in nicely with the types of models typically used with survival data, but that's not the only kind of model I'd like to look at. Thanks, Doug On Mon, 23 Apr 2007, Pikounis, Bill [CNTUS] wrote: Doug, In perhaps similar situations where there are clusters of measurements due to repeated time or space on an individual subject or experimental unit, I have used the survreg() function from the survival library. You can specify left, right, and/or interval censoring within a data set through Surv(), and so I have used left censoring for the LOD observations. I was just focused on marginal or population-averaged estimation, so the use of cluster() in the argument for survreg() and the robust option in survreg() to get sandwich error estimates was sufficient for me. Depending on your needs to evaluate random effects, frailty() in the survival package -- which can be used with survreg() or coxph() --- is another alternative to explore, I believe. Hope that helps, Bill Nonclinical Statistics, Centocor R D -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Douglas Grove Sent: Monday, April 23, 2007 2:29 PM To: Bert Gunter Cc: r-help@stat.math.ethz.ch Subject: Re: [R] fitting mixed models to censored data? Hi Bert, Yes, I am always wary when one software offers something that other do not. The censoring I'm faced with (at present) isn't as complicated as with much 'survival' data. I'm trying to analyze assay data and have a lower limit of detection (LLD) to contend with. Once the level of the analyte gets low enough it can't be accurately quantitated, hence all that is reported is that the level is less than some value (the LLD). So I'm not worried about all the complex assumptions that go along with censoring in clinical trials, etc. Thanks, Doug On Mon, 23 Apr 2007, Bert Gunter wrote: Douglas: AFAIK, this is subject area of active current research. Diggle, Heagerty, Liang, and Zeger , 2002, (ANALYSIS OF LONGITUDINAL DATA) say on p.316: An emerging consensus is that analysis of data with potentially informative dropouts necessarily involves assumptions which are difficult, or even impossible, to check from the observed data. This was ca 1994, I believe, so I don't know whether this view is still held among experts (which I am not). But if it is, you may do well to be careful of whatever SAS does even if you do have to go running off to it. Cheers, Bert Gunter Genentech Nonclinical Statistics -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Douglas Grove Sent: Monday, April 23, 2007 10:58 AM To: r-help@stat.math.ethz.ch Subject: [R] fitting mixed models to censored data? Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that SAS's proc nlmixed can accomodate censoring (though proc mixed apparently can't), so if I can't find something available in R, I'll have to break down and use that. Please, save me from having to use SAS! Thanks much, Doug __ R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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.
