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Date:         Thu, 20 May 2010 15:05:54 -0700
Reply-To:     Dale McLerran <stringplayer_2@YAHOO.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Dale McLerran <stringplayer_2@YAHOO.COM>
Subject:      Re: how to write a repeated measures statement for two blocks of
              time              series
In-Reply-To:  <>
Content-Type: text/plain; charset=iso-8859-1

----- Original Message ---- From: Xin Wei <xinwei@STAT.PSU.EDU> To: SAS-L@LISTSERV.UGA.EDU Sent: Wed, May 19, 2010 5:04:32 PM Subject: how to write a repeated measures statement for two blocks of time series > > three groups of animals are receiving vehicle, drugA and drugB on the > morning of day 1 and day7. After drug administration, their ECG is > continuously measured during the day. therefore, each animal has two > blocs of time series: day1 (post treatment time: 1h, 2h, 4h, 10h....etc) > and day7 (post treatment time: 1h, 2h, 4h, 10h.....etc). I am hoping to > pool the two days' data together for analysis in order to achieve the > best error estimation and statistical power. Apparently, it may not be > appropriate to assume the time correlation structure for day 1 is the > same to that for day 7. It is also wrong to assume the same correlation > between day1_1hr/day1_2hr and day1_1hr/day7_1hr. Unfortunately, I am not > quite sure how to write the correct repeated measures statement for this > unique scenario. Had I only had one day data, it would be > straightforward to write something like the followings: > > proc mixed data=data method=reml covtest; > class id trt time; > model value=trt time trt*time; > repeated time/sub=id(trt) type=AR(1) r; > run; > > any suggestion is appreciated! >

I would suggest that you look at the direct product covariance structures. See the REPEATED statement documentation and search the page for "direct product". Instead of the REPEATED statement that you are using, you might use:

repeated trt time / sub=id type=un@ar(1) r;

The dire product covariance structure assumes a common AR(1) over time for each treatment, and that there is a within-subject correlation between treatments. It seems that this is the sort of covariance structure that you want to use, is it not?

More preferable than the direct product covariance structure would be to use a spatial covariance structure. I would note that the time between measurements following each treatment is not uniform. The AR(1) covariance structure is generally employed when there is a uniform spacing of events over time. If you did have uniform spacing, then for your problem where there are only two treatments, a REPEATED statement using the SP(POWA)(time trt) covariance specification should produce the same results as the direct product covariance structure (assuming an AR(1) model for each treatment) if you coded time as 1, 2, 3, 4, ... rather than coding time as 1, 2, 4, 10, ...

The AR(1) covariance structure assumes that the correlation between hours 1 and 2 is the same as the correlation between hours 2 and 4 and also the same as the correlation between hours 4 and 10 (etc.). That may not be a valid assumption. It may well be that the correlation between hours 1 and 2 is rho, the correlation between hours 2 and 4 is rho**2, and the correlation between hours 4 and 10 is rho**6. The spatial anisotropic power structure would construct the latter model. The appropriate REPEATED statement would for this structure would be

repeated / subject=id type=sp(POWA)(trt time) r;



--------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: Ph: (206) 667-2926 Fax: (206) 667-5977 ---------------------------------------

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