Type I error probability spending for post-market drug and vaccine safety surveillance with binomial data.

ABSTRACT: Type I error probability spending functions are commonly used for designing sequential analysis of binomial data in clinical trials, but it is also quickly emerging for near-continuous sequential analysis of post-market drug and vaccine safety surveillance. It is well known that, for clinical trials, when the null hypothesis is not rejected, it is still important to minimize the sample size. Unlike in post-market drug and vaccine safety surveillance, that is not important. In post-market safety surveillance, specially when the surveillance involves identification of potential signals, the meaningful statistical performance measure to be minimized is the expected sample size when the null hypothesis is rejected. The present paper shows that, instead of the convex Type I error spending shape

SUBMITTER: Silva IR 

PROVIDER: S-EPMC6482950 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature