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05-10-2017 08:48 AM

Attached is the cross tab of my dependent variable DONOR and independent variable BEC. If someone is in the BEC then they seem to donate 18.67 times more than if someone is not in the BEC. I ran a logistic regression:

proc logistic data=work.work;

class BEC (ref='0');

model donor(event='1') = BEC;

run;

and came up with 18.67 odds ratio and a C statistic of .50

Then, I included 19 more variables and ran the logistic regression again. This time, the odds ratio was .426 and the c statistic was .77.

Is something wrong with my model? It doesn't seem like .426 makes sense if you look at the cross tab.

Thank you for any help!

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Solution

05-11-2017
12:37 PM

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05-11-2017 10:51 AM

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05-10-2017 09:32 AM

That is 2x2 contingency table, you should not put PROC LOGISTIC on this data, use PROC FREQ or CATMOD instead.

```
data class;
set sashelp.class;
x=age>14;
run;
proc freq data=class;
table sex*x/ relrisk riskdiff;
run;
```

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05-10-2017 09:36 AM

Wouldn't proc logistic tell me the odds if my dependent variable is binary = Donor (0/1) and then i have a dummy variable?

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05-10-2017 09:50 AM

If you only include one independent variable, that is called perfect predicted model.

Model would not be trusted .

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05-10-2017 09:53 AM

That makes sense since the c statistic was .50 which seems like a flip of a coin. I added more variables and it showed an odds ratio of .426 which would mean that if someone was IN the BEC then they have .42 times less liklihood of being a donor. Does this coincide with the cross tab referece though?

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05-10-2017 10:08 AM

These are very different models. If several of the other 19 variables are correlated with BEC, they will explain much of the same variation as BEC. Also, when you include continuous variables, the odds ratio for BEC is now calculated at the mean value of the continuous variables. In short, you should not expect the odds ratio for BEC in the 20-variable model to match the odds ratio in the one-variable model.

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05-10-2017 11:11 AM

Ah this makes a lot of sense!! I took out two continuous variables and was left with only 18 binary variables and the output gave me 3.5 odds ratio which makes more sense. If i want to include continuous varibales in ivs' should I run a separate logistic regression model with only continuous iv?

Solution

05-11-2017
12:37 PM

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05-11-2017 10:51 AM