Interim analysis

Deze pagina lezen in het Nederlands.

Author: dr. ir. N van Geloven
Authorship

This page was translated from the Dutch-language original using AI and has not been fully reviewed. Where the two differ, the Dutch version takes precedence.

An interim analysis is an intermediate analysis of the study data.

What are the objections to reporting results in the interim?

Next week there is a deadline for a conference. We are considering submitting an abstract with the results so far (26 of the 58 needed patients randomized). However, I don't think I can just do an interim analysis, because:

1. I did not take this into account in my initial power analysis (and I lose power with an interim analysis)
2. The result could introduce a possible bias into the further course of the study. Are my considerations correct?

Your considerations are correct: The problem of losing power with an interim analysis lies in the fact that you then accept a "false positive" probability of 0.05 at two points in time (during the interim and during the final analysis). Together this adds up to a greater probability of incorrectly rejecting the null hypothesis. A responsible way to still be able to do an interim analysis, in the case of a positive outcome, would be to use a stricter significance threshold. In the case of a negative outcome, the trade-off is more complicated. You are still left with the problem that your study is not powered for this number of people. Your bias consideration is also correct. Suppose it is now made public that one treatment works better than the other; via all kinds of bias mechanisms, that could influence the subsequent inclusions (self-fulfilling prophecy, less than full commitment), etc.

References

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