Interim Analysis

In the world of Clinical Trials there is a type of Statistical Analysis they call Interim Analysis. It is concerned with analyzing the results of the trial before its end in order to assure the protocols of the study are being followed and to see whether the results at a given point in time are already strongly favoring or opposing the study hypothesis to the point that the experiment could stop and save money, perhaps saving lives too. For example, if we have strong evidence that treatment A is better than treatment B, then there is no point on continuing the trial, which gives half of participants the less efficient treatment B.

An Interim Analysis differs a little from usual statistical analysis of clinical trials, even though it is a type of statistical analysis. Interim Analysis is not as complete as the statistical analyses performed at the end of the trial and it usually does not aim to understand many outcome variables. One of the reasons is that it is not supposed to be too expensive and another is that it is not supposed to influence the trial. Interim Analysis has to be done preserving the blindness of the trial, therefore in an almost confidential way. Trials with a great amount of analysis before its completion may be questioned as to the extent the external procedures of analyzing data did not influence the trial itself.

As such an Interim Analysis often focus on some main outcome variables at limited points in time. It wants to avoid multiple comparison problems and its protocol must be very well defined in advance. Strict rules of analysis and stopping rules (i.e. if this comparison is found significant the trial must stop) are well defined and followed.

One of the most used types of Interim Analysis is the Group Sequential Analysis. It is defined a priori how many analyses will be performed during the trial and the sample size per treatment at the point when the analyses is done. Based on this stopping rules can be defined in terms of level of significance attained at each and every analysis.

Another type is called Continuous Sequential Statistical Techniques, which, as I see does not go without some criticism. Here, at the arrival of each new results the entire set of data is reanalyzed and differences reaccessed.  Cut off for the lower and upper bound of difference are established that when reached causes the trial to stop because a conclusion is already clear - either the treatment is significantly better or worse than the other. This seems to be a good book on this subject.