General questions and considerations

One question that is frequently asked is what do you do with patients who get given the wrong treatment by mistake It must be said that this does not happen very frequently, but when it does it is necessary to dig a little and try to find out why this has happened. If it is an isolated case and is clearly an administrative error then it seems most reasonable to include that patient in the group according to treatment received. If, however, it is not an isolated case, maybe there are several such mistakes in the same centre, then this draws into question the validity of what is happening at that centre and one starts to think in terms of fraud, has the investigator correctly followed the randomisation scheme In such situations 

The considerations so far in this chapter have been on the evaluation of efficacy. For safety we usually define the safety set as the set of subjects who receive at least one dose of study medication. Usually the safety set will coincide with the full analysis set, but not always. There may well be a patient who started on medication, but withdrew immediately because of a side effect. This patient is unlikely to have provided post baseline efficacy data and so could be excluded from the full analysis set. 

A key aspect of the definition of analysis sets and the way that missing data is to be handled is pre-specification. Usually these points will be covered in the protocol, if not, in the statistical analysis plan. If methods are not pre-specified then there will be problems as the way that these issues are dealt with could then be data driven, or at least there may be suspicion of that. This is, of course, not unique to analysis sets and missing data, but is true more generally in relation to the main methods of statistical analysis. 

To conclude this discussion it is worth covering just a few misconceptions  

This corresponds to similar dropout rates across the treatment groups. The answer to the question is no It is the mix of patients that is the basis of a valid comparison, not the numbers of patients. It is almost inevitable that if 

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