11-02-2016 04:36 AM - edited 11-02-2016 06:22 AM
I would like to assess the relationship between time of day of (protein) intake and overall (protein) intake. Therefore, I want to calculate corelations between the proportion ingested during each hour of the day and the total amount ingested over the entire day. In the end I would like to create a graph such as the one shown in the attachment. In this example, however, time is divided into categories, whereas I want to handle time as a continuous variable.
The difficulty is that I am working with repeated measures (2 observations per participant). I do not know exactly how to handle this in my syntax.
My data looks as follows. So, for each participant I have 48 records (24 hours for each day). Since I want to correlate the proportion of protein ingested during each hour of the day, I created the variable (proportion_protein), by dividing protein_hour by protein_day and multiplying it by 100.
data test1; input subject:12. day:12. hours:12. protein_day:12. protein_hour:12. proportion_protein:12.; datalines; 1 1 8 99 0 0 1 1 9 99 11 11 1 1 10 99 0 0 ... ... ... 750 2 22 56 0 0 750 2 23 56 0.8 1.5 run;
I've thought about the following syntax (adapted from: http://www2.sas.com/proceedings/sugi29/198-29.pdf). However I do not know if I am correct. Do I have to add both proportion_protein and hour into the model, or is this not needed? I have the feeling that if I do not do it, SAS won't know which proportions belong to which hours of the day.
ods graphics on;
PROC mixed DATA=test method=reml covtest plots(maxpoints=none);
Class subject day (ref=first) hour (ref=first);
MODEL prot_day = proportion_protein hour proportion_protein*hour / solution ddfm=kr ;
Random prot_hour /type=un subject=subject v vcorr;
Repeated day / type=un subject=hour(subject) r rcorr;
ods graphics off;
After running this syntax, log says the following:
NOTE: Convergence criteria met.
NOTE: PROCEDURE MIXED used (Total process time):
real time 9.96 seconds
cpu time 9.82 seconds
I do not exacly know if this is the outcome I needed. Also no graphics are shown. Is it even possible to translate this data into a graph as the one shown in the attachement. Or is this not possible? Can someone maybe help me with this? Many thanks in advance.
11-03-2016 08:55 AM
In order to treat day as a repeated effect, you need to include it in the model statement. What happens with something like:
ods graphics on; PROC mixed DATA=test method=reml plots(maxpoints=none); Class subject day (ref=first) hour (ref=first); MODEL prot_day = proportion_protein|hour|day / solution ddfm=kr ; Random prot_hour /type=un subject=subject v vcorr; Repeated day / type=un subject=hour(subject) r rcorr;
lsmeans proportion_protein|hour|day/cl; run; ods graphics off;
Not quite sure about this, because I think you will have a LOT of values for prot_hour, and fitting an unstructured covariance structure may lead to convergence or estimation problems.
Also, this approach assumes that hour is not a repeated measure on a subject, which is probably not a good assumption. You may need to recast day and hour into a single time variable, if you want to fully capture the repeated nature of the design. Alternatively, you might consider day as a random effect, and look at marginal effects over day for proportion protein and hour.