Posting: # 16905
You may have noticed that the latest
I would like to briefly introduce this concept here because (i) there already were several discussions on incorporating previous information into the sample size calculations (see e.g. here or here) and (ii) I believe this can be a valuable tool to assess the probability of trial success. The idea is that the usual power (
I should make some additional comments as this function should not be used without carefully thinking what is behind it. The expected power is sometimes bounded above by a value which is less than one! This often happens when the previous information is simply too unreliable. As a consequence, there is a systematic chance of not achieving the goal/success at all. This is in my opinion important information for the further development of a compound and the decision process. Depending on the importance this may mean that the sample size should be rather (very) high (as compared to the classical power approach) or that a reasonable target power is lower than what is usually considered (a sufficiently high target power may already be for example 70%). In order for the whole team to make an informed decision here, I would always compare the expected power to the classical power (i.e. with
I hope you find this tool valuable in assessing trial success chances. We would highly appreciate any feedback on it!