My analysis is determining the association between weight (continuous variable) and post-operative complications (yes/no). I am doing a multivariable logistic regression analysis, adjusting for seven predictors (1 has multiple categories, 5 are yes/no, and 1 is continuous). I notice that the association between weight and complication is not exactly linear, so it is recommended that I do restricted cubic splines. I was using the following code:
proc logistic data=weight.avg_pred_val_all_data2;
effect spl_weight = spline(weight/naturalcubic knotmethod=percentiles(5));/*natural cubic splines with knots at quartiles*/
model est_prob= spl_weight/ cl;
output out=predicted_values pred=pred;
run;
Where est_prob is the probability of any complication.I then plotted the unadjusted spline association between weight and probability of any complication:
ods graphics/height=6.5in width=10in imagename="Figure2_revised_with_cubicspline" border=off;
proc sgplot data=predicted_values noautolegend;
xaxis label='Weight (kg)';
yaxis label='Any complication' values=(0 to 60 by 5);
series x=weight y=pred/ lineattrs=(color=gray pattern=solid thickness=2);
run;
My question is, how do I show the association between weight and any complication, adjusted for my seven predictors?
If your full model doesn't involve interactions, have a look at proc gam.
Thanks I will try that
Thanks for your suggestions
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