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I am doing a case-control study with 80 disease cases matched 1:3 to non-diseased controls and examining whether they had a binary exposure prior to developing the disease. I am using multivariable conditional logistic regression for the main analysis. My question is, for the "descriptive" or "univariate" table where we try to investigate simple associations of each (non-matched) variable with the outcome, do I just use something like a chi-square test and ignore the matching, or do I use main effects conditional logistic regression (or something else)? Any help would be greatly appreciated.

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I would not use the word "descriptive" statistics here (I would limit "descriptive" and "univariate" to summary or detailed descriptions of single-variables—e.g. histograms, mean and standard deviation, median and IQR, etc.). I would instead use language like "bivariate tests for association."

To test whether prevalence of exposure in cases differed from prevalence of exposure in (3x) controls, I might recommend reading Miettinen's generalization of McNemar's test (McNemar's test is a $\chi^{2}$ test that accounts for the individual matching of the case data with the control data):

Miettinen, O. S. (1969). Individual matching with multiple controls in the case of all-or-nothing responses. Biometrics, 25(2):339–355.

You might also consider Cochran's Q test (which is a nonparametric analog of repeated measures/blocked design ANOVA for nominal data).

Cochran, W. G. (1950). The comparison of percentages. Biometrika, 37(3/4):256–266.

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  • $\begingroup$ Thanks, both of those a good suggestions (and you are right of course about the bivariate/univariate thing). $\endgroup$ – user2414716 May 14 '14 at 15:00
  • $\begingroup$ Feel free to click the check mark if you feel this is an acceptable answer. $\endgroup$ – Alexis May 14 '14 at 19:36

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