# Correlating various diagnoses with amount of weight gained

I have a processed dataset where patients took a medication and gained a varying amount of weight. In addition, any ICD9 diagnoses the patients have received while being monitored are recorded. I'd like to see if certain diagnoses are correlated with amount of weight gained. What would be a good procedure for testing this?

Of note, each patient has several diagnoses: there are about 1,200 unique diagnoses in total.

Currently, I have calculated the average wt. gain along with the standard deviation, then grouped the patients by standard deviation, say:

(not actual #'s)
Total Patients: 420
Total in Group 1 (>2 Sd weight Loss): 10
Total in Group 2 (>1 Sd weight Loss: 50
Total in Group 3 (<1Sd weight gain or loss): 300
Total in Group 4 (>1 sd weight Gain): 50
Total in Group 5 (>2 sd weight Gain): 10


I was thinking of looking at how many patients have x,y,z (etc.) diagnoses in each group. Say Type II DM:

Total: 200 Pts Have it
In Group 1: 4/10 Pts have it
Group2: 15/50 Pts have it
etc...


With this method, would simply computing a Pearson correlation on the absolute count of Diagnoses vs. wt change group be appropriate? Additionally, if by the time I get to Group 2 or Group 4 0 patients carry a particular diagnoses, how will this impact running the analysis this way?

Appreciate any pointers.

• Do you have the actual weight measurements, or is it just those categories? (I hate this medical approach of taking an arbitrary threshold and classify everybody either above or below it; economists, at least, would've broken this down to five quintiles and have the same number of people in each group.) I would think that the s.d. needs to be computed within the diagnosis, rather than across everybody, to make the standardized effect sizes comparisons meaningful. Aug 12, 2011 at 21:51
• Yes, have the original weights; can break them down into as many groups as necessary. Aug 13, 2011 at 13:45