ArticleStatistics in medicine2026
Sequential Event Rate Monitoring.
Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Sequential Event Rate Monitoring.Statistics in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective monitoring of event rates is essential for maintaining statistical power and study integrity in clinical trials, particularly when the primary endpoint involves time-to-event outcomes. We propose Sequential Event Rate Monitoring (SERM), a new and innovative approach for continuous monitoring of event rates. SERM leverages the Sequential Probability Ratio Test (SPRT) with improved boundaries derived from the nonlinear renewal theorem by Kim and Woodroofe (2003). This method represents the first practical implementation of their theoretical work in this area. SERM offers several tangible benefits, including ease of implementation, efficient use of data, and broad applicability to trials. Decision boundaries can be directly obtained from simple formula. A detailed illustration of the method using real-world data from a large Phase III clinical trial demonstrates its potential for rapid assessment. SERM operates on blinded data so it can be used in tandem with a broad range of study designs while preserving study integrity. Although slow patient accrual lengthens the time needed to reach a conclusion, it does not significantly affect type I or type II errors associated with the decision. This new method provides a robust tool for enhancing trial monitoring, enabling timely and informed decision-making in diverse clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.