Hello Community, I am working with a data set that includes 3 variables: patient ID number (patient_identifier), a self-reported pain score 0-10 (pain_score), and the number of days since the beginning of a treatment that a pain score was obtained (time_pain). I have provided an example of what the data looks like below. Note that pain scores being analyzed are only those that were obtained at a clinic visit during the time that a patient was receiving the treatment. Therefore, a patient who discontinued treatment relatively early will have fewer pain scores than a patient who received treatment longer. The number of pain scores per patient also depends on whether pain was assessed at their appointments. Further, the number of days since the start of treatment that pain was assessed will also vary between patients depending on when they had follow-up appointments. I would like to obtain subject-specific estimates of pain intercepts and pain-time slopes (to be used as predictor variables of treatment duration in a subsequent model). To obtain the individual estimates of pain intercepts and time slopes, I am using a hierarchical linear model, via PROC MIXED, given that repeated measures of pain scores are nested within individuals. However, the issue is that my current model is not providing estimates for the individual slopes- I have provided my code and Covariance Parameter Estimates output below. Could someone please help with how I might be able to obtain these effects from the model? Any help with this would be much appreciated!! Data input: Patient_identifier Time_pain Pain_score 001 0 9 001 35 7 001 82 7 001 144 8 001 145 7 002 0 10 002 20 10 002 21 8 003 0 0 003 4 0 003 18 0 004 0 0 004 1 0 proc mixed data=data_have covtest noclprint method=ML;
class patient_identifier;
model pain_score=time_pain/ solution ddfm = SATTERTHWAITE;
random intercept time_pain/ sub=patient_identifier type=un solution;
ods output solutionr=sr;
run;
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