[R] fitting mixed models to censored data?
dgrove at scharp.org
Mon Apr 23 23:51:07 CEST 2007
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.
On Mon, 23 Apr 2007, Pikounis, Bill [CNTUS] wrote:
> 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,
> Nonclinical Statistics, Centocor R & D
>> -----Original Message-----
>> From: r-help-bounces at stat.math.ethz.ch
>> [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Douglas Grove
>> Sent: Monday, April 23, 2007 2:29 PM
>> To: Bert Gunter
>> Cc: r-help at 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.
>> On Mon, 23 Apr 2007, Bert Gunter wrote:
>>> 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.
>>> Bert Gunter
>>> Genentech Nonclinical Statistics
>>> -----Original Message-----
>>> From: r-help-bounces at stat.math.ethz.ch
>>> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Douglas Grove
>>> Sent: Monday, April 23, 2007 10:58 AM
>>> To: r-help at stat.math.ethz.ch
>>> Subject: [R] fitting mixed models to censored data?
>>> 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,
>>> R-help at stat.math.ethz.ch mailing list
>>> PLEASE do read the posting guide
>>> and provide commented, minimal, self-contained, reproducible code.
>> R-help at stat.math.ethz.ch mailing list
>> PLEASE do read the posting guide
>> and provide commented, minimal, self-contained, reproducible code.
More information about the R-help