- Thread starter ivanext84
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Thank you very much for your interest and help.

Ivana

Initially, we thought of doing only a Cochrane Q-test and others tell me that a mixed model is better.

The sample is 807, 200 per arm approximately,

The standard error of a proportion would be something in the region of 0.0005 (less than 1/1000) for each

group. Or, is the parasite extremely rare (or extremely frequent, respectively), so that regardsless of

the enormous sample sizes, one outcome group consistes of only some dozen cases or so?

By the way, did you identify each subject over time, so that you can say something like "our subject No.

145,790 initially had a parasite, then at t1 this subject had no parasite, and at t2 he still had none"?

With kind regards

Karabiner

Sorry...I misspelled the numbers, it is actually 807 participants

If you want to compare the groups over time, then Cochran's Q is useless, since it

compares between time points for one group only. Moreover, if this is an observational

study without randomization, you perhaps want to include additional variables to

adjust for group differences.

You could indeed consider a mixed model. Or you could perform logistic regression analyses,

for example predicting

With kind regards

Karabiner

Oops, I did mis-read that.

If you want to compare the groups over time, then Cochran's Q is useless, since it

compares between time points for one group only. Moreover, if this is an observational

study without randomization, you perhaps want to include additional variables to

adjust for group differences.

You could indeed consider a mixed model. Or you could perform logistic regression analyses,

for example predicting*outcome at t2* by *group* and *outcome at baseline*, or predicting

*outcome at t3* by *group* and *outcome at t2*.

With kind regards

Karabiner

If you want to compare the groups over time, then Cochran's Q is useless, since it

compares between time points for one group only. Moreover, if this is an observational

study without randomization, you perhaps want to include additional variables to

adjust for group differences.

You could indeed consider a mixed model. Or you could perform logistic regression analyses,

for example predicting

With kind regards

Karabiner