Played around with this and I think I understood the explanation  on https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect015.htm But I cant see where " lower=LCL_High upper=UCL_High is coming from.    I keep on getting error messages saying these are not found, ... View more

This is actually quite interesting. Ive approached this model , and similar outcomes from various different directions.   Have between 15 and 20'ish covariates  that I am using, and they are all clinically relevant, and "mostly " still highly significant in the full models. All of those covariates had a p value well below 0.05 in univariate analysys. In addition to linear interpretations, I ve run quadratics on the Blood Pressure measures, and splines. And when I made my final tables I compared the odds of specific levels (Centiles 5th, 10th, 25th, 50th, 75th and 95th) compared to the odds at the mode to get my OR's.   In all of these with the fully adjusted spline models the blood pressure measures give significant odds. I have n=1992 patients in my database, and someone else has already published results from a different pool but with n=250k patients. They published OR curves for splines before and after asjustment, and the adjusted DBP's were still signifiant.    Thats where I am now, Just trying to get meaningful adjusted figures for the quadratic curves or the splines. Whats interesting to me is that depending on how the previous authors adjusted their models , there may be grounds for questioning their methods . Specifically at what level they adjusted the categorical variables. Clearly when the categorical values are 0, we get narrowish confidence intervals in then adjusted model. When the categorical values are 1, the Confidence intervals are extremely wide... so adjusted models are useless perhaps?   Having said that, when I add just one adjusted variable (eg gender or age), we still get the fuzzy lines so for a partially adjusted model we might need a different approach. ... View more

It looks like all the categorical values in the prior model were having figures drawn as if all their values were 0.   So I tried this instead: ods graphics on; %let charvar = Sex_num CABG_i Valve_i Redo1 HTN Hyperlip CHF Afib CM MI AICD DDRF ASA Steroids Amio LMWH Digoxin AA1 Statin1; proc logistic data =HEERDT.VPICU_label plots(only)=effect(x=Ind_SBP1 connect yrange=(0,1) cl ); class &charvar.; model VP24_VasoNE_012(ref='0') = Ind_SBP1|Ind_SBP1 Age BMI pre_Na pre_BUN pre_Cr pre_Gluc pre_Neutro pre_Lymph pre_Hgb &charvar. / nooddsratio ct covb clodds=wald; output out=QuadSBPVpAdj predicted=Fit lower = l upper = u; /* output predicted values for graphing */ effectplot fit; where VP24_VasoNE_012 NE 1; run;   What I was trying to do is see what would happen of I made those categorical values appear to be numeric, and use the macro to set a value for them between 0 and 1. Looks like the value was taken as 1, and the CI's are so wide now that the output figure is meaningless.  Don't even know what to do if I cant present any adjusted models. ... View more

Ok, So it sounds as though you are saying that with all the adjusting variables in the fulll model, the categorical values should be fixed to one value, and the continuous should be fixed to a mean (or come generally accepted representative value).   I am trying to create two models: 1) just quadratic. 2) Quadratic, and all the confounding variables.   I'm confused here,  because if you look at the accompanying output, the first part clearly shows smoothed out predicted values. I wanted to have more control of the appearance such as having the chance to edit the colours, and to get rid of the gridlines. Thats the whole point of using proc sgplot. It works fine in just the quadratic model but I don't know what is going on that I need to compensate for in the output from sgplot rather then the logistic step.   ... View more

I was responding to your suggestion and that of PGStats. Basically I gave a logistic model with a dichotomous outcome. I have a continuous  independent variable. It works better as a spline or a quadratic polynomial rather than as just a linear interpretation(I actually wanted to try out fractional polynomials, but have no idea how to do that).   I have nice curves for the splines and the quadratic, but when I add extra covariates went highly irregular zig zag lines, so thats not presentable. How do I get publication quality adjusted figures? I think I might have got these working before with the sgplot using loess rather than series =x.   Does this make any sense?    ... View more

Reeza:   What do I have to do with an effectplot statement? ... View more

I think I only partially understand this. What would I do with a score or effectplot statement to smooth this curve out? Even when I add one covariate the curves before very jagged and fuzzy.    ... View more