I have searched extensively for a solution to the following problem, without success.

I have used multiple imputation on SPSS 22 to create 5 datasets which have a number of variables that predict patient outcome.

As mentioned in a number of previous threads, SPSS does not pool the analysis for certain types of tests, though these can be done by hand using Rubin’s method: http://sites.stat.psu.edu/~jls/mifaq.html#howto

I have used this approach (checked and re-checked multiple times, and checked the original Rubin texts) and my result seems incorrect. In short, the overall standard error is far higher than in any of the 5 datasets or the original dataset. When I used the same pooled analysis to compare one ROC curve to another ROC curve (pooling the standard error for the differences in the areas of the ROC curves) the result is far from significant. However, in the original dataset and the 5 imputed sets, the p value for the comparison is highly significant: <0.0001.

I would have assumed that an analysis using imputed data would not result in far wider 95% CI and a higher p value then a complete case analysis (the original dataset)?!

Is Rubin’s approach not accurate for ROC curves (though the literature is full of people “pooling” ROC curves citing his approach) or have I made a simple error?!

Any help is greatly appreciated. ]]>

I have the following question:

In my study I compare two kinds of surgery options. Group A has 9 patients and group B has 6 patients. When comparing the seperate complications there is no significant difference. But now I want to compare the total of complications between group A and group B.

Group A (9 pt): total 11 complications in 6 patients (3 patients had no complications

Group B (6 pt): total 2 complications, both in the same patient (5 patients had no complication)

Which test suits my question best?

Thanks ]]>