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Hi,
i am trying to find association between post op intolerance (PO_Intolerance__Y_N_1)- dependent variable and pattern of sewing during surgery (LOCATION_OS1)- exposure. Both have two categories.
PO_Intolerance__Y_N_1: yes vs no
LOCATION_OS1: entire vs partial
SAS code i used is:
proc logistic data=red.sleeve7;
class PO_Intolerance__Y_N_1(ref="N") LOCATION_OS1(ref="partial");
model PO_Intolerance__Y_N_1= LOCATION_OS1/scale=none aggregate;
run;
OR i received is :
Odds Ratio Estimates | |||
Effect | Point Estimate | 95% Wald | |
LOCATION_OS1 entire vs partial | 0.182 | 0.107 | 0.310 |
please see attched for details of output.
I assumed that post op intolerance will be more likely in entire sewing group vs partial sewing group.
But the results are opposite.
Please let me know if it sample size played a role? numbers in each group played a role? or it is statistically sound to report the results?
Thanks.
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Check your log. Specifically look at the notes to see what was modeled as the outcome, perhaps that is coded reverse to what you expect.
You can specify the outcome desired by using the effect option.
model varName (effect='no') = .... ;
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Hi!
with the code below;
proc logistic data=red.sleeve7;
class PO_Intolerance__Y_N_1(ref="N") LOCATION_OS1(ref="partial")/param=reference;
model PO_Intolerance__Y_N_1= LOCATION_OS1/scale=none aggregate;
run;
output remains the same. Please see attched for details.
SAS Output
0.182 | 0.107 | 0.310 |
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Check your log. Specifically look at the notes to see what was modeled as the outcome, perhaps that is coded reverse to what you expect.
You can specify the outcome desired by using the effect option.
model varName (effect='no') = .... ;
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Hi!
Thank you for your help. I checked my dataset and there was mistake in coding. OR is now greater than 1 which i expected and it is insignificant which i believe is due to small numbers in Post op intolerance yes group.
the output now is
SAS Output
N | 340 |
Y | 13 |
SAS Output
1.405 | 0.451 | 4.383 |
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I am sorry but i do not know what a prior or oversampling is. Do you have any material on that i can read. Thanks for your help.
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https://gking.harvard.edu/category/research-interests/methods/rare-events
https://statisticalhorizons.com/logistic-regression-for-rare-events
Search for : "logistic regression rare events" for more material.
For SAS specific articles, search on lexjansen.com
@Kyra wrote:
I am sorry but i do not know what a prior or oversampling is. Do you have any material on that i can read. Thanks for your help.