Evidence map›Paper›PMID 41572588›Full record

ArticleStatistics in medicine2026

Sequential Event Rate Monitoring.

Dong-Yun Kim, Sung-Min Han

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Sequential Event Rate Monitoring.Statistics in medicine · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Dong-Yun KimNational Institutes of Health, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0001-9405-4840
Sung-Min Hani2KIE, LLC, Gaithersburg, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Clinical Trials, Phase III as TopicData Interpretation, StatisticalHumansModels, StatisticalProbabilityResearch Designclinical trialcontinuous event rate monitoringsequential probability ratio teststaggered entrytime‐to‐event‐data

Identifiers

PMID41572588
PMCPMC12828109

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.