Hi Rick, I have a binary outcome variable called adherence(codes as 0 and 1). I ran the codes you suggested. Here is my code. proc logistic data=cases plots=effect; class sex1 race group age1 hmo ER FmsScore; model adherence(event="0")=sex1 race group age1 hmo ER FmsScore; output out=LogiOut predicted=PredProb; /* save predicted probabilities in data set */ run; proc rank data=LogiOut out=LogiDecile groups=10; var PredProb; ranks Decile; run; /* Then compute the mean predicted prob and the empirical proportions (and CI) for each decile */ proc means data=LogiDecile noprint; class Decile; types Decile; var adherence PredProb; output out=file mean=obs_risk PredProbMean lclm=Lower_CI uclm=Upper_CI; run; proc sgplot data=file noautolegend aspect=1; lineparm x=0 y=0 slope=1 / lineattrs=(color=grey pattern=dash); loess x=PredProbMean y=obs_risk; /* if you want a smoother based on deciles */ scatter x=PredProbMean y=ob_risk / yerrorlower=Lower_CI yerrorupper=Upper_CI; yaxis label="Observed Probability of Outcome"; xaxis label="Predicted Probability of Outcome"; run; Here are the issues I am facing(I have attached a csv file for you) 1) There are unequal number of observations in each decile 2) my observed risk and predicted risk are very different in each group because of which I am not getting the plot correct. Please help. Thank You again.
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