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;      ... View more

Hello,  I am trying to merge two data sets with different observations (Data1 (fulldata_pca_2021)=2760 with lifestyle variables, Data2 (blood301774)=1774 with blood biomarkers). A new data set (Data3=fulldata_pca_2021a), as expected, had 2760 observations and all lifestyle and blood biomarkers. However, Data3 (fulldata_pca_2021a) gave higher observations for blood biomarkers (eg.2462 for low density lipoprotein level) than supposed to be 1774 or less.   Below is a SAS code I used to merge: Proc sort data = blood301774; by codea; run; Proc sort data = fulldata_pca_2021a; by codea; run; Data fulldata_pca_2021a; merge fulldata_pca_2021 blood301774; by codea; run; NOTE: Format M211A was not found or could not be loaded. NOTE: Format MUSPGENA was not found or could not be loaded. NOTE: MERGE statement has more than one data set with repeats of BY values. NOTE: There were 2760 observations read from the data set WORK.FULLDATA_PCA_2021. NOTE: There were 1774 observations read from the data set WORK.BLOOD301774. NOTE: The data set WORK.FULLDATA_PCA_2021A has 2760 observations and 25 variables. NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.03 seconds Here is a proc means to check mean LDL in Data3; 3933 Proc means data = fulldata_pca_2021a; 3934 Var ldl ; 3935 run; NOTE: There were 2760 observations read from the data set WORK.FULLDATA_PCA_2021A. NOTE: PROCEDURE MEANS used (Total process time): real time 0.03 seconds cpu time 0.00 seconds   Analysis Variable : ldl LDL Cholesterol (mmol/L) N Mean Std Dev Minimum Maximum 2462 2.8373680 0.8309344 0.3000000 8.9000000 Thanks     ... View more