Evidence map›Paper›PMID 39865327›Full record

ArticleStatistics in medicine2025

Improving the Effectiveness of Sample Size Re-Estimation: An Operating Characteristic Focused, Hybrid Frequentist-Bayesian Approach.

Ping Gao

Abstract read
In one paragraph

Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Ping GaoBiostatistics, Innovatio Statistics Inc., Bridgewater, New Jersey, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sample size re-estimation (SSR) is perhaps the most used adaptive procedure in both frequentist and Bayesian adaptive designs for clinical trials. The primary focus of all current frequentist and Bayesian SSR procedures is type I error control. We propose a hybrid frequentist-Bayesian SSR approach that focuses on optimizing operating characteristics (OC), which uses simulations to investigate the associated OC and adjusts accordingly. The hybrid approach incorporates the Bayesian predictive power into the frequentist framework of SSR. Simulations show that the hybrid approach can substantially outperform popular frequentist type error-focused SSR procedure. The hybrid approach can substantially improve the effectiveness of SSR using Bayesian predictive power.

Indexed as

Clinical Trials as TopicBayes TheoremComputer SimulationHumansModels, StatisticalResearch DesignSample SizeBayesianconditional powerhybrid adaptive designmodified predictive powerpredictive powersample size re‐estimation

Identifiers

PMID39865327
PMCPMC11771723

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Registered trials

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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.