Dear All,
I am trying to examine the effect of the fat intake at 30 years on the BMI change over 9 years.
My data had some dropouts which I wanted to account for using Proc GEE with Missmodel statement to calculate weights that are used for reducing biased estimates related to dropouts. I can see we need to create two variables (Prevy and Ctime) to prepare the analysis (SAS Help Center: Example 47.3 Weighted GEE for Longitudinal Data That Have Missing Values). But the explanation for creating Prevy is for the binary outcome (Amenorrhea status) in the previous three-month interval given this study was conducted every three months over a year at 4-time points (See the code below). My question is how can I create Prevy for a continuous outcome. Thank you in advance for your support.
Kind Regards,
Tolassa
data Amenorrhea; set Amenorrhea; by ID; Prevy=lag(Y); if first.id then Prevy=0; Time=Time-1; Ctime=Time; run;
It does not matter what value you use as long as it is a valid value for the distribution of the response. In other words, you could still use 0 as long as 0 is in the support set for the DIST= option even though you won't actually observe a 0 value.
The code you present should work whether Y is continuous or categorical, I believe.
SteveDenham
It does not matter what value you use as long as it is a valid value for the distribution of the response. In other words, you could still use 0 as long as 0 is in the support set for the DIST= option even though you won't actually observe a 0 value.
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