ArticleStatistics in medicine2018
Type I error probability spending for post-market drug and vaccine safety surveillance with binomial data.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- A lesson in embracing uncertainty in early safety signals from the Tsepamo study.AIDS (London, England) · 2025Article
- Exact sequential test for clinical trials and post-market drug and vaccine safety surveillance with Poisson and binary data.Statistics in medicine · 2021Article
- Exact sequential analysis for multiple weighted binomial end points.Statistics in medicine · 2020Article
- Alpha spending for historical versus surveillance Poisson data with CMaxSPRT.Statistics in medicine · 2019Article
- Exact conditional maximized sequential probability ratio test adjusted for covariates.Sequential analysis · 2019Article
- Conditional power as an aid in making interim decisions in observational studies.European journal of epidemiology · 2018Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
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 conventionally used in clinical trials, a concave shape is more indicated for post-market drug and vaccine safety surveillance. This is shown for both, continuous and group sequential analysis.
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Registered trials
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