I have a health status scale measured at 2 different timepoints (baseline and 1 month) with 5 different response levels (0-4). I want to see if there is a statistically significant difference in within group change from baseline to the 1 month time point for each of these response levels. My questions are as follows:
1. Am I using the right code to accomplish this task?
2. If so, which statistics do I report to highlight if the difference is significant?
3. Finally, I think the procedure below gives an overall p-value and not a level-by-level p-value and statistics. How do I test a level by level difference between baseline and 1 month response? Like the difference between response level 0 between baseline and 1 month.
I am running the following proc freq code (analogous to paired t-test for continous variables):
proc freq data=health;
tables hs_BASELINE*hs_1_MONTH / agree norow nocol nopercent;
run;
The result is as follows:
SAS Output
|
|
Statistics for Table of hs_BASELINE by hs_1_MONTH |
Test of Symmetry | |
---|---|
Statistic (S) | 328.8670 |
DF | 10 |
Pr > S | <.0001 |
Kappa Statistics | ||||
---|---|---|---|---|
Statistic | Value | ASE | 95% Confidence Limits | |
Simple Kappa | 0.1402 | 0.0139 | 0.1130 | 0.1674 |
Weighted Kappa | 0.2379 | 0.0171 | 0.2044 | 0.2714 |
Effective Sample Size = 1577 Frequency Missing = 325 |
WARNING: 17% of the data are missing. |
Was it repeated measure problem ?
Check PROC CATMOD for contingency table.
Especially the example in its documentation.
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