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 using  Reduced Rank Regression (RRR) to derive dietary patterns using 33 food groups with 5 response variables. I am using the following SAS code: proc pls data=RRR method=RRR nfac=5 ; model Response1  Response2  Response3  Response4  Response5  = group1-group33; output out=pattern xscore=scorex yscore=scorey; run; This gave me correlation loading plot (in the form of radar plot) which I am not familiar with to interpret with confidence. I wanted to see loading coefficients for each factor score produced by this procedure. How can I get this, please? You support is highly appreciated and thanks in advance for your support.       ... 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

Hi there, I am trying to calculate stabilised inverse probability weights to account for missing related to loss-to-follow up in my cohort study. I calculated this and it had a mean value of 1. Then I repeated the analysis using the calculated weight (ipw) as can be seen in the following proc genmod. My question is that is the weight statement put correctly in proc genmod below? I am obating the output and the results seem meaningful, however, I want to hear from experts in SAS. Thank you in advance for your time and support.  Proc genmod descending data =data; class covariates; model chol_status30=covariates /dist = binomial link=log lrci type3; Covariate1/exp; Covariate2/exp; . . . weight ipw; run;   ... View more