Hello,

I wish to know how I can determine the number of observations for an outcome variable (case or control) by an independent variable (intervention) that has been incorporated in an adjusted logistic regression model for a case-control study.

In this example the model has dropped 7 observations due to "predicting failure perfectly". So I would need to show a total of cases/controls x intervention which also has excluded these seven observations.

I have installed the "distinct" module, but I don't know how to apply it to a regression analysis as opposed to an entire dataset. Perhaps this is the wrong approach.

Any help is appreciated.

Thank you
Jocelynne

logistic outcome independent_1 i.age_mth_test_grp i.family_smoking_miss i.state2 i.month if year==2016 & tested_7d==0 & age_mth_test_grp<5
note: 2.state2 != 0 predicts success perfectly
2.state2 dropped and 4 obs not used

note: 5.state2 != 0 predicts failure perfectly
5.state2 dropped and 3 obs not used


Logistic regression Number of obs = 63
LR chi2(15) = 12.08
Prob > chi2 = 0.6728
Log likelihood = -33.330255 Pseudo R2 = 0.1534

-------------------------------------------------------------------------------------
outcome | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
independent_1 | 1.022343 .7859253 0.03 0.977 .2265874 4.61272
|
age_mth_test_grp |
1 | 3.119581 2.807651 1.26 0.206 .5345734 18.20477
2 | .9349802 1.190389 -0.05 0.958 .0771041 11.33776
3 | 2.846745 3.486589 0.85 0.393 .2581238 31.39562
4 | 2.495358 2.854672 0.80 0.424 .2650723 23.491
|
|
family_smoking_miss |
1 | 7.982311 9.941517 1.67 0.095 .6950327 91.67524
9 | 4.259453 9.634958 0.64 0.522 .0505733 358.7456
|
state2 |
2 | 1 (empty)
4 | 1.123285 .9685987 0.13 0.893 .2072555 6.087986
5 | 1 (empty)
|
month |
6 | .5933001 1.113463 -0.28 0.781 .0149899 23.48274
7 | .1460062 .2688005 -1.05 0.296 .0039563 5.388294
8 | .3355456 .587782 -0.62 0.533 .0108314 10.39485
9 | .0646431 .129849 -1.36 0.173 .001261 3.313871
10 | .2031119 .4800371 -0.67 0.500 .001977 20.86772
|
_cons | .7504512 1.38808 -0.16 0.877 .0199935 28.16803
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