Hello
We conducted a matched case control study.
For each case, 2 control subjects are matched.
These 2 control subjects are from 2 different clusters.
---------------------------------------------------
Data:
group case control_clust1 control_clust2
1 exposed non-exp non-exp
2 exposed non-exp non-exp
3 non-exp non-exp non-exp
4 non-exp non-exp non-exp
5 non-exp non-exp non-exp
6 non-exp non-exp non-exp
7 non-exp non-exp non-exp
8 non-exp non-exp non-exp
9 non-exp non-exp non-exp
10 non-exp non-exp non-exp
11 non-exp non-exp non-exp
---------------------------------------------------
First, I compared between case and control_clust1,
to see the effect of exposure, as in:
. mcci 2 9 0 11
| Controls |
Cases | Exposed Unexposed | Total
-----------------+------------------------+------------
Exposed | 2 9 | 11
Unexposed | 0 11 | 11
-----------------+------------------------+------------
Total | 2 20 | 22
McNemar's chi2(1) = 9.00 Prob > chi2 = 0.0027
Exact McNemar significance probability = 0.0039
Proportion with factor
Cases .5
Controls .0909091 [95% Conf. Interval]
--------- --------------------
difference .4090909 .158186 .6599959
ratio 5.5 1.570118 19.26607
rel. diff. .45 .2319678 .6680322
odds ratio . 1.973826 . (exact)
Naturally, comparison between case and control_clust2
generates the same result.
Q1. Can I describe this result in the manuscript as
"OR = 1.97 (P<0.0027) based upon McNemar's chi square"?
This seems peculiar because there is no confidence
interval for the OR.
************************************************** ******
Next, I aggregated the two control clusters, and
used clogit, as in:
. clogit disease exposure,group(group)
Iteration 0: log likelihood = -12.084735
Iteration 1: log likelihood = -9.8875106 (not concave)
Iteration 2: log likelihood = -9.8875106
Conditional (fixed-effects) logistic regression Number of obs = 33
LR chi2(0) = 4.39
Prob > chi2 = .
Log likelihood = -9.8875106 Pseudo R2 = 0.1818
------------------------------------------------------------------------------
disease | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exposure | 2.39e+20 . . . . .
------------------------------------------------------------------------------
Q2. This result seems more peculiar!, because mcc showed
a highly significant result (P<0.0027).
What was wrong?
Your assistance would be appreciated.
Yosh
We conducted a matched case control study.
For each case, 2 control subjects are matched.
These 2 control subjects are from 2 different clusters.
---------------------------------------------------
Data:
group case control_clust1 control_clust2
1 exposed non-exp non-exp
2 exposed non-exp non-exp
3 non-exp non-exp non-exp
4 non-exp non-exp non-exp
5 non-exp non-exp non-exp
6 non-exp non-exp non-exp
7 non-exp non-exp non-exp
8 non-exp non-exp non-exp
9 non-exp non-exp non-exp
10 non-exp non-exp non-exp
11 non-exp non-exp non-exp
---------------------------------------------------
First, I compared between case and control_clust1,
to see the effect of exposure, as in:
. mcci 2 9 0 11
| Controls |
Cases | Exposed Unexposed | Total
-----------------+------------------------+------------
Exposed | 2 9 | 11
Unexposed | 0 11 | 11
-----------------+------------------------+------------
Total | 2 20 | 22
McNemar's chi2(1) = 9.00 Prob > chi2 = 0.0027
Exact McNemar significance probability = 0.0039
Proportion with factor
Cases .5
Controls .0909091 [95% Conf. Interval]
--------- --------------------
difference .4090909 .158186 .6599959
ratio 5.5 1.570118 19.26607
rel. diff. .45 .2319678 .6680322
odds ratio . 1.973826 . (exact)
Naturally, comparison between case and control_clust2
generates the same result.
Q1. Can I describe this result in the manuscript as
"OR = 1.97 (P<0.0027) based upon McNemar's chi square"?
This seems peculiar because there is no confidence
interval for the OR.
************************************************** ******
Next, I aggregated the two control clusters, and
used clogit, as in:
. clogit disease exposure,group(group)
Iteration 0: log likelihood = -12.084735
Iteration 1: log likelihood = -9.8875106 (not concave)
Iteration 2: log likelihood = -9.8875106
Conditional (fixed-effects) logistic regression Number of obs = 33
LR chi2(0) = 4.39
Prob > chi2 = .
Log likelihood = -9.8875106 Pseudo R2 = 0.1818
------------------------------------------------------------------------------
disease | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
exposure | 2.39e+20 . . . . .
------------------------------------------------------------------------------
Q2. This result seems more peculiar!, because mcc showed
a highly significant result (P<0.0027).
What was wrong?
Your assistance would be appreciated.
Yosh
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