```Date: Wed, 31 Mar 2004 12:41:09 -0500 Reply-To: Paul Kononoff Sender: "SAS(r) Discussion" From: Paul Kononoff Subject: Missing data at the end of my curve Hello folks, I have sort of an unusual challenge. I am mining a data set which tests the effects of a treatment on a response variable (back fat) that employed repeated measurements. The experimental unit was a beef steer. The challenge is that once an animal reached a specific weight it was slaughtered. I am trying to determine the effect of treatment on the rate of back accumulation. I have two problems: 1. Unfortunately the animals that grew the quickest, and with the steepest slope, were killed the earliest. Obviously I have more measurements on animals that grew slower. The points that went unmeasured for slaughtered animals were coded as missing data. Is this approach correct? 2. I also wanted to illustrate these results in a graph and have used an LSMEANS statement to generate my points. The resulting estimates at the later time points seem very unreliable and biased, as they are heavily influence by slower growing animals. How should I determine estimates for these time points? Maybe I can't Any advice would help. For those of you interested I have included my code below. Thanks in advance! PJK proc mixed data=UofS method=reml; class PEN GT trt time; model BF = GT time GT*time /noint solution ddfm=satterth; random pen; repeated time/type=arh(1) sub=tag(pen); LSMEANS GT/diff; LSMEANS GT*time/slice=time diff; /** estimate 'CC v CT' GT -1 1 0; estimate 'CC v TT' GT -1 0 1; estimate 'CT v TT' GT 0 -1 1; estimate 'slopes CC v CT' GT*v_date -1 1 0; estimate 'slopes CC v TT' GT*v_date -1 0 1; estimate 'slopes CT v TT' GT*v_date 0 -1 1 ; estimate'slope overall' v_date 1; estimate 'slope CC' v_date 1 GT*v_date 1 0 0; estimate 'slope CT' v_date 1 GT*v_date 0 1 0; estimate 'slope TT' v_date 1 GT*v_date 0 0 1; run; Paul Kononoff Project Director Ruminant Feed Analysis Consortium University of New Hampshire Durham, NH PH: 603-862-1815 ```

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