[R-sig-ME] missing data in lme, lmer, PROC MIXED

M Henry H Stevens HStevens at muohio.edu
Mon Jul 28 13:04:19 CEST 2008

Thanks Ken. I have been assuming that they meant missing covariates (a
subject provided most of the predictors, but not all). So I take it that
SAS does no imputation on its own-that the user would need to do that
(if they wanted?). lme does not do anything like that.


On Sat, 2008-07-26 at 22:39 -0400, Ken Beath wrote:
> On 26/07/2008, at 7:28 AM, M Henry H Stevens wrote:
> > Hi folks,
> > I have colleagues who comfortably state that "missing data" are ok in
> > "mixed models" - because "the program (PROC MIXED) handles missing
> > data
> > -- I have a hard time imagining what it does.
> >
> > To those of you who use both R and SAS, I was wondering if you might
> > share insight into what these do.
> >
> > As far as I know, for lme:
> > 'na.action="na.omit" ' or na.exclude, removes the rows with any
> > missing
> > data.
> >
> This depends. If the missing data is the dependent and it is missing
> at random then as mixed models are fitted using maximum likelihood it
> will produce results that are optimal. Roughly (there are some really
> technical definitions for missing data and I haven't checked them) if
> we don't know the outcome and the reason it is missing isn't due to
> its value or the other data then we can simply leave it out of the
> likelihood equation it as it has no useful information. A problem is
> when data being missing provides this sort of information and is very
> difficult to model. An example is if observations above a certain
> value are more likely to be missing.
> An alternative method of dealing with repeated data is to produce a
> summary for each subject or cluster, for example by averaging the last
> three visits. This doesn't correctly handle missing data although the
> loss in efficiency is usually small and it can work well, provided
> only a small proportion is missing.
> What R and SAS don't deal with directly is missing data in the
> covariates. This takes a bit more work, for example using multiple
> imputation. Here the complete case method where an observation with
> any missing data is removed will result in a loss of efficiency
> compared to what can be achieved.
> Ken

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