Quite so! This would suggest, though, that one should obtain multiple random draws and use the techniques that SAS has available for analyzing multiply imputed data.

Dale

--------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: dmclerra@NO_SPAMfhcrc.org Ph: (206) 667-2926 Fax: (206) 667-5977 ---------------------------------------

--- On Fri, 9/10/10, Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US> wrote:

> From: Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US> > Subject: Re: ESTIMATE statement in Proc MIXED > To: SAS-L@LISTSERV.UGA.EDU > Date: Friday, September 10, 2010, 9:39 AM > I agree. There are situations > where the LOCF could have merit. > > One caution. If you can, you might want to use LOCF > with some > variability (i.e., normal noise around a reading, variance > of all > upper/lower LOCF, etc.). Otherwise you could end up > reducing the > overall variance in the data and falsely increasing the > sensitivity to > detect treatment effects. > > Warren Schlechte > > -----Original Message----- > From: John Whittington [mailto:John.W@mediscience.co.uk] > Sent: Friday, September 10, 2010 11:13 AM > To: Warren Schlechte; SAS-L@LISTSERV.UGA.EDU > Subject: Re: ESTIMATE statement in Proc MIXED > > At 09:55 09/09/2010 -0500, Warren Schlechte wrote: > >In surveys, we often think that those who return the > surveys likely > >respond differently than those who do. As such, > we try to correct for > >this non-response bias. if we can detect trends > in the response rates > >associated with time of return, we can model the > missing data > >(non-returns). I wonder if you couldn't do > something similar here, > >where the missing data are imputed based on a model of > what you do > >observe. For example, maybe patients stop coming > in once they get > well, > >so missing data reflect the resolved cases. Or, > maybe they get too > sick > >to come in, so they reflect the terminal cases. > In either case, > looking > >at the data to see if some model explains the > missingness may help you > >model the missingness. > > > >I'm no expert in this field, but I recently saw a talk > where the > authors > >used the idea of having model hyperparameters within a > Bayesian setting > >to help account for the missing data, where missing > data were not MAR. > > I think that one certainly has to do something about > 'informative' (or > potentially informative) missing data (i.e. not MAR) - > since to simply > ignore it (i.e.treat it as 'missing') can lead to very > biased and > potentially very misleading results. As I wrote in > response to Dale's > comments, this is a situation which I come across > frequently in the > context > of clinical trials, but it is much more difficult to deal > with (and has > a > smaller literature) than missing data which is MAR. > > In the context of clinical trials, my personal inclination > is often > that, > although widely criticised (because of potential bias), use > of the 'last > > observation carried forward' (LOCF) approach may be the > least of the > evils. A common situation is that in which treatments > are given to > control > or modify a measurable or assessable 'outcome' quantity > (e.g. blood > pressure, blood sugar, pain level or whatever). It > will often happen > that > serial measurements of the outcome show a progressive > deterioration up > to > the point at which it is deemed necessary or appropriate to > remove the > subject from the trial (and treat more effectively), with > the effect > that > all subsequent measures are missing. If one uses the > LOCF approach, all > > subsequent measurements will be deemed to be the same as > the one which > resulted in the subject's discontinuation - which perhaps > reasonably > reflects the real-world situation in which one would not > leave a patient > on > a treatment if they were progressing to even less > acceptable situations. > > In a situation such as I've described, it may well be > possible to model > the > progressive deterioration of the patient, and thereby to > obtain > extrapolated estimates of what results would have been > obtained had the > patient continued to receive the treatment - and, in one > sense or > another,

> I guess that's what most imputation methods would be > seeking to > achieve. However, if those extrapolated > values/imputations are > unrealistic > in terms of the real world (i.e. they would never be > allowed to arise, > and > may not even be compatible with life), I have to question > whether this > approach is necessarily appropriate, even if it is less > open to > statistical-theory-based criticism than is LOCF. > > That's how I see it, anyway. > > Kind Regards, > > > John > > ---------------------------------------------------------------- > Dr John Whittington, > Voice: +44 (0) 1296 730225 > Mediscience Services > Fax: +44 (0) 1296 > 738893 > Twyford Manor, Twyford, > E-mail: John.W@mediscience.co.uk > Buckingham MK18 4EL, UK > ---------------------------------------------------------------- >